Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Authors: Zhen Zhang
Bingsheng He
Venue: NeurIPS 2025 poster ()
Keywords: Graph Neural Networks
Unsupervised Graph Domain Adaptation
Open-set Graph Domain Adaptation
Primary area: general_machine_learning
TL;DR: We propose a novel framework called GraphRTA, which conducts reprogramming on both the graph and model sides for open-set graph domain adaptation.

Abstract: Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domain adaptation models primarily focus on the closed-set setting, where the source and target domains share the same label spaces. However, this assumption might not be practical in the real-world scenarios, as the target domain might include classes that are not present in the source domain. In this paper, we investigate the problem of unsupervised open-set graph domain adaptation, where the goal is to not only correctly classify target nodes into the known classes, but also recognize previously unseen node types into the unknown class. Towards this end, we propose a novel framework called GraphRTA, which conducts reprogramming on both the graph and model sides. Specifically, we reprogram the graph by modifying target graph structure and node features, which facilitates better separation of known and unknown classes. Meanwhile, we also perform model reprogramming by pruning domain-specific parameters to reduce bias towards the source graph while preserving parameters that capture transferable patterns across graphs. Additionally, we extend the classifier with an extra dimension for the unknown class, thus eliminating the need of manually specified threshold in open-set recognition. Comprehensive experiments on several public datasets demonstrate that our proposed model can achieve satisfied performance compared with recent state-of-the-art baselines. Our source codes and datasets are publicly available at https://github.com/cszhangzhen/GraphRTA.

Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Zhen Zhang
Bingsheng He National University of Singapore Singapore
'Najjing University
National University of Singapore zhen zhangānju edu.
dcshebânus. edu.
Abstract
Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domain adaptation models primarily focus on the closed-set setting, where the source and target domains share the same label spaces. How- ever, this assumption might not practical in the real-world scenarios, as the target domain might include classes that are not present in the source domain. In this paper, we investigate the problem of unsupervised open-set graph domain adaptation, where the goal is to not only correctly classify target nodes into the known classes, but also recognize previously unseen node types into the unknown class. Towards this end, we propose novel framework called GraphRTA, which conducts reprogramming on both the graph and model sides. Specifically, we re- program the graph by modifying target graph structure and node features, which facilitates better separation of known and unknown classes. Mcanwhile, we also perform model reprogramming by pruning domain-specnn speciffc parameters to reduce bias towards the source graph while preserving parameters that capture transfer- able patterns across graphs. Additionally, extend the classifier with extra dimension for the unknown class, thus eliminating the need manually specified threshold in open-set recognition. Comprehensive experiments on several public datasets demonstrate that our proposed model can achieve satisfied performance compared with recent state-of-the-ar baselines. Our source codes and datasets are publicly available at https: //github com/ Saliaannzzhe//rraahi//
Introduction
Graph Neural Networks (GNNs) have demonstrated impressive capabilities a wide range of graph- based tasks, such as node classification [25, 14, 48]. social recommendation [54, 6, 57], molecular generation [59, 34, 44] and point cloud processing [22, 43, 45], etc. Despite their great success, these GNN models often suffer from severe performance degradation when confronted with distri- bution shifts in graphs, such as changes in the underlying structures, node features, and label dis- tributions [52, 30, 28). Unsupervised Graph Domain Adaptation has become promising strategy for addressing the domain shift problem by transferring knowledge across domains without relying on labels in the target domain. The majority of existing models developed under the closed-set setting [62, 51, 66], which assumes that the source and target domains share the same set of classes.
However, such strict assumption is unrealistic in real-world applications, since the target domain might introduce new classes that are absent from the source domain [64, 53], leading to significant challenges in identifying unseen samples. For instance, fraud detection models trained previ- ously known fraudulent behaviors from the source domain might struggle in the emerging target do- main, as fraud schemes continually evolving with fraudsters frequently developing new tactics.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Therefore, treating all novel category instances as known classes will significantly compromise the model's 's ability generalize across diverse environments. Towards this end, open-ser graph domain adaptation [49, 33] has been proposed to accurately classify known node types into their correspond- ing classes while simultaneously identifying unseen node types into an unknown class. promotes better generalization capabilities, making the model applicable across variety of applications.
There exist some recent endeavors to explore the unsupervised open-set graph domain adaptation task [49, 40, 33, 32, 53J. The main procedure involves dividing target instances into the known and unknown groups based on their prediction entropy, then aligning the known group with the source domain. To distinguish between these two groups and enable open-ser set recognition, a predefined threshold is often employed, assuming that the unknown instances will exhibit higher entropy than the known ones. While promising, these methods depend heavily on manually set threshold and one threshold cannot all, which makes them difficult to adapt to different distributions. Addition- ally, they also struggle with learning clear decision boundaries, since they mainly focus on aligning source domain with the target known group, which may result in inadequate separation of the tar- get unknown group. Hence, more efforts are necessary to tackle the challenges of open-set graph domain adaptation, particularly in recognizing and separating the target unknown group.
To address the aforementioned challenges, we propose a novel framework named GraphRTA (Reprogram To Adapt), which performs dual reprogramming from the graph and the model per- spectives. Specifically, we reprogram the target graph by refining its structure and node features to explicitly reduce the distribution shift and encourage clearer separation between the known and unknown groups. At the same time, we reprogram the model by pruning domain-specift parame- ters based on their gradients, thereby mitigating bias towards to the source graph, while retaining parameters that capture transferable patterns between the source and target graphs. Furthermore, we augment the classifier by adding an extra dimension for the unknown class, which removes the necessity for manually specified threshold in open-set recognition. Extensive experiments across multiple public datasets indicates that our proposed model achieves superior performance in com- parison to the latest state-of-the-art baselines.
In summary, our key contributions are as follows:
We explore the challenge of unsupervised open-set graph domain adaptation, which is more prac- tical in the real-world scenarios yet remains under-explored in the graph community.
We are the first to reprogram both the model and the graph to improve the alignment and separa- tion processes, offering an architecture-agnotii solution that can be applied across various GNN architectures.
Experimental results show that GraphRTA outperforms or matches the SOTA baselines, highlight- ing the effectiveness of our approach in addressing the challenges associated with the open-set graph domain adaptation task.
2 Related Work
Graph Neural Networks. During the past decade, GNNs have demonstrated remarkable capabil- ity in tackling a wide range of graph learning tasks. Following the message passing framework, numerous types of GNNs have been developed, which can be broadly classified into spectral and spatial approaches [56, 50, 1]. Spectral methods, such as ChebConv [4] and Spec-GN [58], de- rive graph convolution operator based on spectral graph theory. contrast, spatial models like GCN [25], GraphSAGE [14] and GAT [48] perform convolution by directly aggregating informa- tion from neighboring nodes. For comprehensive insights into these models, readers may refer to comprehensive surveys on GNNs [55, 23]. Despite their impressive performance, GNNs often de- pend on high-quality labeled data, which could be challenging to obtain in real-world applications. Additionally, their performance can significantly deteriorate when encountering distribution discrep- ancies. To mitigate this limitation, recent work has focused on adapting models trained on label-rich source domains to unlabeled target domains, thereby enhancing their generalization capabilities.
Closed-Set Graph Domain Adaptation. Although domain adaptation has been extensively investi- gated in computer vision [11, 60, 40] and natural language processing [39, 5, 61], research on graph domain adaptation remains in its early stages. Current graph domain adaptation methods primarily aim to learn domain-invarian representations, typically employing statistical matching techniques

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such as maximum mean discrepancy [13] or central moment discrepancy [63], or by adopting ad- versarial learning mechanism [38] for implicit alignment. More specifically, UDAGCN [52] utilizes combination of local and global graph encoders alongside adversarial training to achieve domain- invariant representations. GRADE [51] introduces graph subtree discrepancy metric to reduce dis- tribution shifts between source and target graphs, while SpecReg [62] applies spectral regularization to support theory-grounded graph domain adaptation. StruRW [30] proposes an edge re- re-weighting strategy to mitigate conditional structure shifts. A2GNN [28] highlights the inherent adaptability of graph neural networks by decoupling its transformation and propagation layers. Nevertheless, all of these aforementioned models assume that both the source and target graphs share the same label space [65, 29). Unfortunately, this is impractical in real-world scenarios, as the target graph may contain new classes that do not exist in the source graph.
Open-Set Graph Domain Adaptation. Open-set domain adaptation extends closed-set domain adaptation by recognizing target novel classes that are not present in the source domain, meanwhile accurately classifying target instances that belong to the source label space [36, 41, 27, 26]. Re- cent models utilize threshold to designate low-confidence samples unknown, while aligning the source domain with the known portion of the target domain via adversarial training [19, 49, 33, 18]. Among them, DANCE [40] applies self-supervised neighborhood clustering to align each target sample with either a neighboring instance or a source prototype. PGL [33] utilizes a progressive approach to gradually reject target samples and align conditional distributions through episodic training. OpenWGL [53] introduces an uncertainty-
node representation learning framework that employs constrained variational graph autoencoder to filter out unknown instances with multi-sampling approach. OpenWRF [16] integrates out-of-diitributiin detection techniques with neighborhood information from the graph identify novel classes. G2Pxy [64] generates both the internal and external unknown proxies via mixup to predict the distribution of novel classes in open-set learning. SDA [49] groups target representations into several clusters and employs a sep- arate domain alignment strategy to align each target sample with either a target cluster center or a source prototype. In contrast, our model is designed from both model-centric and data-centric perspective, which effectively generalizes to the target domain.
3. The Proposed Model
3.1 Notations and Problem Definition
For unsupervised open-set graph domain adaptation, we are given a labeled source graph 93 = (X,, As, Y,) containing n, nodes and an unlabeled target graph G. (X., Ar) with n, nodes, where the source and target graphs are sampled from different probability distributions, i.e., P(G.) o ((G.). The feature matrix X E R= represents node attribute information, and the adjacency matrix A E RT indicates the connectivity information between nodes. The source graph includes set of classes C. forming the node label matrix Y, E xIC,1 Mcanwhile, the target graph is associated with an additional set of classes Crr.. collectively labeled as 'unknown', resulting in a total of |c,| = |C,l + classes. We decompose the GNN model (6-) into two fundamental parts: the feature extractor fao (-), which transforms the graph into the node representation space, and the classifier g (.). which assigns class labels based on these node representations. Therefore, the GNN model (-) can be represented as +- = fw 96- Our problem can then be formulated as follows:
Given a graph neural network 4. a labeled source graph G. with label set C. and an unlabeled target graph Gt with label space C. where C, is a subset of Ct our goal of unsupervised graph domain adaptation is to train the model using Gs and G, to accurately classify target nodes when they belong to a label in C, while marking nodes as 'unknown' if their labels fall outside of C.,
3.2 Graph Neural Networks Revisiting
Current GNNs operate within the message passing framework [25, 14, 48), which performs convo- lution by iteratively aggregating representations from its local neighborhood. Taking GCN [25] as an example, the node representations at layer can be calculated follows:
Z = (G, w aD ; AD Zz' -W'))
(I)
3

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Nodel Reprogramming
Geaph Reprograrming
M IV
syslae
Souzcce Classi Fior
CC
Source
Domain Classifior
Torgen, Korm
OWN
Reprogramaed
target draph
Reprogranmed Parget draph
GRX
Hoighto
Leazned Nor Edgea
Classitior
Updated Hodo
Fentures
Darget Graph
Figure 1: The pipeline of the proposed GraphRAT model. In the model reprogramming module, some nodes and connections are faded, indicating that these weights have been pruned during the reprogramming process. In the graph reprogramming module, node features are updated, and edges are dynamically modified (deleted or added), as indicated by dashed lines. Additionally, domain adversarial learning is incorporated to categorize instances into three distinct groups: source, target- known, and target-unknown, thereby enhancing the model's generalization capacity.
Here, at.) denotes the non-linear activation function, i.e., ReLU or LeakyReLU, The matrix À A + I represents the adjacent matrix with seff-connections, while D is the diagonal degree matrix of Å. w E Rd::-xxd; indicates matrix of trainable parameters. As demonstrated in Section 4.3, our proposed model is architecture agnostic and can be employed across diverse GNN frameworks.
3.3 Donain-Agnostic Model Reprogramming
We present an overview of the proposed GraphRTA framework Figure 1. Unsupervised open- set graph domain adaptation involves training model on a labeled source graph and an unlabeled target graph, where the target graph contains novel classes not present in the source graph. Since the model is trained using labeled data from the source graph while having no access to labels in the target graph, a primary challenge is the risk of bias toward the souree domain. This discrepancy can lead the model to overemphasize source-specifie patterns, thereby limiting its ability to generalize effectively to the target domain.
Motivated by the lottery ticket hypothesis [9, 2, 35, 3], which demonstrates that only a subset of parameters is crucial for generalization, we propose reprogram the graph neural network (-) by selectively masking its weights. Specifically, insignificant elements within the weights W of each layer. Therefore, the node representations Z layer is computed follows:
introduce differentiable masks M' to indicate the
z' fw W OD 7 AD + 1(W' OPOO OM)),
(2)
where @ denotes the element- weieent-wise product. Hyperparameter p is utilized to control the sparsity of the masks, determining the proportion weights to be retained. To quantify the importance of each weight element, calculate its gradient VM' with respect to the loss function. The absolute value of these gradients is then used as the importance score, reflecting how much each weight contributes to generalization in the target domain. Weights with smaller gradients typically contribute less to reducing the loss and may capture domain-specific patterns that do not help in adapting to the target domain and recognition of unseen classes. By masking such weights, the reprogrammed model can focus better on transferable features that generalize across domains. Hence, we set the lowest percent of gradient values in M' to zero, leaving the remaining elements at These sparse masks are then applied to prune W', resulting in the reprogrammed sparse model.
To handle open-set recognition, further reprogram the output layer by augmenting extra di- mension for the unknown class as follows:
( (6 2, 2 2],
where Φ E Cdx represents the closed-set classifier and Rdx1 denotes the linear projec- tion layer for the unknown class. The augmented logits are then passed through softmax layer to generate the posterior probabilities, with the final prediction assigned to the class with the highest probability. This mechanism is designed to effectively distinguish between known and unknown

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classes through using a dynamic threshold based on the input node representation z, whereas exist- ing models typically rely on a manually defined threshold, making them less adaptable domain discrepancies. Through these model reprogramming procedures, we develop a domain-agnostic GNN model capable of identifying open-set classes.
3.4 Distribution-Awars Graph Reprogramming
While our proposed model reprogramming module can help mitigate source bias, it does not address the fundamental domain shift that arises from the structural and feature differences between the source and target graph data. Existing approaches often overlook the fact that the domain shift is inherently caused by the input graph's 's unique characteristics. This discrepancy makes it challenging for the GNN model to generalize well across domains in the open-set scenario, as the target graph may contain novel structures, features or classes that the source domain does not encompass.
To overcome this limitation, further propose performing graph reprogramming, where the target graph itself is refined to improve compatibility between the source and target domains. Through this approach, we modify the target graph's 's structure and node features better align with the source domain, while differentiating the known and unknown groups within the target domain [66]. This strategy not only mitigates domain shift but also strengthens the model's ability to generalize and recognize the unseen classes. More specifically, we implement graph reprogramming by applying transformation functions to adjust both the target graph structure and node features:
= = A, = via (At).
(4)
where (-) represents the transformation function applied to update node features, and (+(+)) de- notes the function for modifying the graph structure by adding or removing edges. Although nu- merous approaches, like graph structure learning methods [7, 21, 31]. be employed to adjust the graph data, we choose two simple, direct transformation strategies described below, with additional options explored in the ablation study in Appendix B.
For node features, we define the transformation function vz = X AX, where AX, E R. represents a set of continuous, learnable parameters. This simple formulation allows for either masking node features (setting them to zero) or modifying their values, enhancing the models flexibility in refining node representations. For graph structure, the transformation function (.) is modeled as via (At) graph's 's structure by adding or removing edges. The operation @ denotes element-wise exclusive OR (XOR), where if both the corresponding values in A, and AA, are 1, the XOR operation results in O, effectively deleting the edge. Conversely, if one value is and the other is 1, an edge is added. To regulate the changes to the graph structure, impose constraint modifications made to the adjacency matrix. Particularly, the sum of the entries in A , is limited by much
A, @ AA., where AA, Rnex*t is binary matrix that adjusts the
the total number of
pre-defined budget B, i.e., AA, S B. preventing excessive changes that might deviate from the original graph and ensuring computational efficiency. Through these graph reprogramming processes, dynamically update the target graph mitigate distribution shifts and facilitate open- set recognition.
3.5 Training Procedure
In this section, we detail the process of updating the model reprogramming and graph reprogram- ming modules using the proposed losses. Following previous works [12, 20, 33, 46), we adopt domain adversarial learning framework to match the source and target distributions through feature alignment. In an open-set scenario, where the target domain contains unknown classes not present in the source domain, such alignment approach can cause negative transfer due to class set mismatches. Existing methods focus aligning source and target features within the known class set, ignoring any alignment signal from target instances that belong to unknown classes. As a result, the classifier is unable to establish clear decision boundary for unknown classes, as target-unknown instances remain entangled with known ones in the aligned feature space.
To this end, we aim at explicitly pushing target-unknown features apart from both the source and target-known features, while ensuring alignment between source and target-known features. Specif- ically, we first calculate the entropy value for each target instance based on the known classes as
5

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follows:
[0,1
e, = H(p.) - = - Pie (p()),
(S)
where p; is the output probability generated by the classifier go(+). The entropy value, which quan- tifies the uncertainty, serves as an indicator for open-set recognition, where a high entropy suggests the instance may belong to the unknown class. After normalizing the entropy values, we model them as being generated by mixture of two Beta distributions to capture the overall characteristics of the target graph [20]:
p(e=)= PeR p(e:) lik) + Pea p(es](u),
where e represents the entropy value for node v/s p(ez]tk) and p(e/(tu) denote the probability density functions for the target-known (tk) and target-unknown groups (tu), respectively. Meanwhile, Pat and you are the mixing coefficients for these two distributions. Then, we perform posterior
inference by fitting Beta mixture model using the
(EM) algorithm:
Pakk p(es](k)
p(thle))
Pekk p(c, + Peu p(e,)tu)
Thus, we estimate the probability that instance belongs to the target-known group based on its entropy value without using any thresholds and p(tu[es) = 1 - p(tklei).
After estimating the probability of each target instance belonging to either the target-known target- unknown group, we can classify all the instances into three distinct domains for the domain adver- sarial learning framework, i.c., source, target-known, and target-unknown. To achieve this goal, we introduce a domain discriminator de implemented as a multi-layer perceptron (MLP), which en- gages in minimax game with the feature extractor (-)..). The feature extractor works to learn node representations that make it challenging for the discriminator to identify the origin each node. We implement adversarial training using Gradient Reversal Layer (GRL) [12], which promotes the maximization of feature representations. Meanwhile, the domain discriminator d+(-) is optimized by minimizing the cross-entropy loss effectively classify domains follows:
+h+ 3
Lado
(8)
n1
+
where y, [1, 0, 0] when the node is from the source graph, and y, = [0, p(tkeee)), p[tule;]] when the node is from the target graph. ÿ: represents the domain prediction for node v, Thus, the ad- versarial learning loss simultaneously aligns and segregates the three sets to learn domain-invariant representations.
Furthermore, we update the model reprogramming module by utilizing the label information in the source graph as follows:
The Ldss > Loo (x))))))))))) + Ao (xx;) |C,| +1),
1=1
where Lee represents the cross-entropy loss, while ((-) denotes the reprogrammed model. À is trade-off hyper-parameter The first term focuses on optimizing the augmented output match the ground truth labels, thereby preserving performance on the closed set. For the second term, (bxx) Wy removes the probability associated with the ground truth label and aligns with class |C,|+1, which can be regarded as simplified mixup approach. By masking out the ground truth, we ensure that the model is explicitly trained to classify instances as unknown when they do not align with any of the known class patterns. For graph reprogramming module, we incorporate entropy minimiza- tion loss to encourage confident predictions for the unlabeled target instances, while simultaneously distinguishing target-known features from harget-unknowe features as follows:
Lent H(((x:)) (8s,
(10)
where H(--) is the entropy function defined Eq.( 5), while minimizing Lee(S (o,((e,)((tuee)) enables the graph to generate discriminative features specifically for target-unknown instances. Therefore, the overall loss function is:
L = Ladv Lote + Lent-
(11)

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4 Experiments
4.1 Experimental Settings
Datasets. To thoroughly assess the perfor- mance of our proposed GraphRTA, we conduct experiments using three categories of publicly available datasets. An overview of these dataset characteristics is provided in Table I, with more detailed descriptions as follows.
Table I: Dataset statistics.
Datasets
#Nodes
#Edges
#Feat
#Class
DBLPv7
5,484
8,117
Citationv1
8,935
15,098
6,775
5
ACMv9
9,360
15,556
The first category comprises three citation datasets, i.e., DBLPv7(D). Citationv I(C), and ACMv9(A) [28], where nodes represent indi- vidual papers, while edges indicate citation relationships. Particularly, DBLPv7 encom- passes DBLP papers published between 2004
ogbn-arxiv
169,343
1,166,243
128
40
Cornell
183
298
Texas
183
325
1,703
5
Wisconsin
251
515
and 2008, Citationv1 contains articles from Microsoft Academic Graph up to 2008, and ACMv9 consists of papers published by ACM from 2000 to 2010. Each paper is classified into one of the five distinct research topics: Databases, Artificial Intelligence, Computer Vision, Information Secu- rity, and Networking.
We further include the ogbn-arxiv dataset [17], which is composed of computer science papers from arXiv. Each paper is represented by a 228dimmeniional feature vector, derived by averaging the embeddings of the words in its title and abstract. We partition this dataset into three temporal domains according to the publication years: 1950-2016 (Arxiv I). 2016-2018 (Arxiv If), and 2018 8- 2020 (Arxiv III). The task involves classifying each paper into one of 40 predefined subject areas under the temporal shifts.
Lastly, we incorporate the WebKB dataset [37], a webpage dataset from computer science depart- ments across various universities. Among them, we select three heterophily graphs (i.e., Cornell, Texas, and Wisconsin), where nodes represent web pages and edges indicate hyperlinks between them. Each webpage denoted by ong-of-words features, then we categorize them into the follow- ing five groups: student, project, course, staff, or faculty.
Baselines. We compare our proposed GraphRTA against a broad set of recent baselines across three key categories. (I) Graph Neural Networks: This category includes traditional GNN models like GCN [25], SAGE [14], and GAT [48]. They trained the source graph and then evalu- ated directly on the target graph without adaptations between domains. For open-set recognition, a threshold is applied to identify instances belonging unseen categories. (2) Closed-Set Graph Do- main Adaptation: Approaches in this group focus on graph domain adaptation within the closed-set settings, where the source and target graphs share the same label space. We compare our model with several recent methods including UDAGCN [52], GRADE [51], SpecReg [62]. StruRW [30]. and A2GNN [28]. Similarly. we employ a predefined threshold to identify open-set instances. (3) Open- Set Graph Learning or Domain Adaptation: This group of methods is designed for scenarios where the target graph contains categories that do not exist in the source graph. We consider DANCE [40], OpenWGL. [53], PGL [33], OpenWRE [16]. G2Pxy [64], SDA [49] and UAGA [42] for comparisons. These approaches are strong baselines for assessing our model's 's capability to transfer knowledge to the target domain and generalize effectively to unseen categories.
Implementation Details. In this work, we adopt the experimental setup used in prior researches [64, 49], where portion of classes is reserved as "unknown" with the remaining classes regarded as "known". Specifically, |C. is 3 in Citation and WebKB datasets, and 30 for the ogbn-arxiv dataset. The domain adaptation models are trained using labeled source nodes from the known classes along with unlabeled nodes from the target graph. Among them, 70% of the labeled source nodes are utilized for training, 10% are set aside for validation, and the remaining 20% serve as a sanity check. The final evaluation conducted on the target nodes. For a fair comparison, we utilize the baselines' publicly available source codes and tune their hyperparameters to their optimal values using the validation set. Our proposed GraphRTA is implemented using PyTorch Geometric [8] and optimized with the Adam optimizer [24]. Hyperparameters for learning rate, weight decay and À are searched within the ranges of 10.1, 0.01, 0.001, le le-1, le 15-5), and the sparse constraint p is explored within the interval [0, 1]. The experiments repeated five times, and performance metries are reported as the
7

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Table 2: Node classification accuracy and H-score (mean + std) for citation datasets. results are shown in bold with the second-best best results underlined.
The best
Models
I
Cination
ACMv9
Citaioon I DBBLPV7
ABMM
Ace
HS
Aec
HS
Ace
HS
Acc
HS
Ace
HS
Acc
HS
GEN
40.6110.98 41.022.11
45
50.201125
1226
47.1010.49
49.05.1074
51.810022
56 56.1310..22
43.90-1.50
2447
39.2610.70
07.9611.34
SAGE:
28.3420.00 26.89:22100
10
41.77
45
14:
43.9021550
1.56
45.0342.15
18
47.1420822
51.65-0.33
4166020.7
120621132
3262200222
40.32:00,40
GAT
+2.01+0.73
31,51=2.36
54
8484+1
25
12.00+2.66
16,
0.666
30.67+1
66
34.56 4456--0664
32.22
1,53
35.85-1.91
25
25.66+5.00
30
32.87+141
20.91+3.92
UDAGEN
44.7624.112
20,9626.21
55.07
50.0527.00
1.53
53.
33.562600
62.3644.19
45.211:4,05
47.28
09
39:21:1116
52.38.1.18
46.9226.26
GRADE
57,23+11.6
59,49-1.16
12+066
14141+10 1.07
0.8600020
6044+0025
64.21+00.4
54,93-0.40
57.7H+0441
64.00+0.44
57.36+0.55
SpecRez
51,3115,60 16.70
16.58
63.881582
59,6210.59
61.12.1886
55/721243
19,417.41
ShuRw
46,47:4.63
42,36=4425
46.911195
460883.00
51.9-0000
15.3000113
56.19200.10
$4.0661.19
48.00-00022
43.85=1774
51.00-991
43.25=1.29
A2GNN
42,53=2.07
41.66=3.69
60.4++0.52
62:74:00.0
57.21:11003
57:12:0063
63.45:0.31
65.37550.50
57.64-1822
60.68:2.27
01:00:113
43.52=11.2
DANCE
52.77=01.64
60,94200,76
58.01:00:77
01.31:00.62
59, 76
61,33:0055
02:972:1665
65,42:1.20
55,97-00.22
58.91:00000
55.77:20.56
58,95:20,70
OponWen
49.96+00.62
5:57+11.56
52.45+0022
7.56+0..0
48 08.57-0.50
407:0099
1,49-0.36
48.04-1.06
25 13+1344489
40 40.52+0068
22.05+2.29
PGL
$6.4211.04
57,86.11.12
48.451.1122
53.1511:23
$1,87.0.99
54.7110.72
53.8310.66
59,0110.775
48.27_.0.00
51,7610833
52.82.0.57
36.1610.91
OpeaWRF
53,5842.59
31.15=44.57
48 16:661133
35.45=3422
4.501=3.52
33 33.0866646
12
31
31,96t 1.24
47.27=11.54
19.38=4005
17.31=1.14
34.3ke9.77
G2Pay
59.75+:0.49
14
56 73
41:3::2411
58 49:4 1.03
58.5611
36
61 47
59,
13::0.54
54 04.58-0.58
54 84:22:0:77
78 56
56.36::0.69
14.02+1.755
SDA
18.37F4877
59,9
59,97=6.74
59 19.06-4775
56
57.
53-6.22
S8 85-55
58 63.55+0.91
65.53+2..00
57.66-0.61
60
56+++ 70
57.02 07.37++2.72
59.71+1877
UAGA
53.3716.72
61.3411.15
22.11
67.50.1..95
52.25
59.5955995
52.5115.3
64.114.55
18.14
35.75
47.91.9244
21.161215
GraphRITA
66.26.10.93 66.33.1.69 62.3310.68 65.4211.10 2..93.2.63
638711997 65.9911887 56.91 59.41.1222 60.111.998 62.33
Table 3: Node classification accuracy and H-score (mean + std) for ogbn-arxiv and WebKB datasets.
OOM means out-of-memory. '-' indicates cases where baseline methods encounter errors because their predefined strategies not satisfied.
Models
Arii Arsiv II
Arsiv I-+Arsi III
Arsis II Areiv III
Wiseenenn
Texas Tornell
Tesas-Wascome Wiscomsin
Ace
HS
HS
Acc
Acc
HS
GCN
46.00.0.20
44.06.0.74
41,57.1022
41
41.0611089
21:1910:77
0.2010.45
21
13.32.19115
SAGE
44.9500315
37,83-0.71
42.750015
346340164
49.0=0011
38.
18568236
1070
34.75-331
11.66:4911
14,48=9901
GAT
44.81
94.31=1.30
42,05+0.00
30 34.77+0766
76 46.49-0.17
36,55-078
20.6+00211
0,4000.54
20.97=3.00
15
15899+9.1
9:4--2.11
8.08-2.24
UDAGGN
31,9032.277
33.68:23.48
27.71::086
28.3711:3
35.0911.229
19.53
19:2815657
5.9425.51
29,18=1.87
7.35
22.78:5.19
3.93-4.12
GRADE
+0.20 47.18-00.29
50 39.78-0.37
44 19+0044
42 10-00-1
46.09-021
17.05+115
12,52+1.26
28.96-7.44
21.52-11.5
74.66+7-32
23.84-44.67
SpecReg
37,50.1.990 31,76.1..64
28 16.14.99
20011357
16:8010.29
31.701427
20.7911324
19.0113.12
31.8913.34
13.53.99155
19:20.1..72
10.28116.14
SmuRw
37,47=1.93 40.67=:2.19
16.77:0277
401.442:1445
42.10=00.4
43.62=0050
16.57*2226
16:22633.7
42.02::7.51
40.9::776
16.01=2247
11,4626617
A2GNN
42,07+0.14
45.00=0..24
$8.9200
16 43.14:0.11
42:26-0.53
45.18-0117
19.12+2.56
17,40+3.32
44.37-0.71
31
44.59:6644
14:96:0..77
6,22-2.58
DANCE
GUM
0OM
OUM
OUM
0OM
OOM
21.52+23.4
3,1120,89
20.77=0.000
0000000000
4.222:0.21
1.651.50
OpenWen.
32.58-+0.58
1.45+0040
$2.09=144p
133--0.37
15.4-=261
004+008
16.57422.49
14.09+2.30
31,22-5.77
28.09+565
+0.51++4.00
8.76-1.56
PGL
41,5010.25
46.3210.288
0022810:24
45.4310277
18.2417.16
0.0010.00
21.20.00:45
00010000
25.551990
0:00.0000
OpaiWRF
32.56=0.55
1.45#0.40
$2.99:1499
1.31=11.77
35.46=261
0043000
262997.111
6.8425.81
21.64=381
4.5%=5.03
26336114
5.99=4.65
GJPxy
31,13:::555 18.45=1.133
24 70:01
16:08:3325
34.33-303
37,27:675
SDA
29.771:0.34
42.60.0.15
36.371:011
39.4411.21
41.53-00.20
46.03:10188
UAGA
32,92+0.116
23.69=0.16 52 12:2440.13
22.08+0.13
39. 19.16-0.59
20.79:0441
GraphRIA 47.7011.39 50.79.2.79 45.5212.00 10.40 $2.37 48.42.194 33461443 34.611776 52.18.0.35 35.661083
29 20.08
mean along with standard deviations for both accuracy and H-score [10]. The H-score combines the accuracies of the target known and target unknown classes to provide a balanced assessment of the model's 's performance as follows:
HS 2 Accek X Accia
(12)
Accek + Acoes
where Acctk denotes accuracy on the target known classes, and Acc,, indicates accuracy on the target unknown classes. A higher score means balanced performance across known and unknown target categories, offering a more comprehensive evaluation metric.
4.2 Results and Analyses
We present the overall results in Table 2 and Table 3. As we can see, our proposed GraphRTA con- sistently demonstrates superior performance across a variety of scenarios. Within the three baseline categories, standard GNN models show limited performance, due to their lack of mechanisms to address distribution shifts between source and target graphs. Closed-set adaptation baselines, which account for these distribution shifts, yield better results than standard GNNs but struggle to handle open-set classes effectively. In contrast, open-set basclines generally perform well by incorporating strategies to recognize and manage previously unseen classes in the target graph, though they are still outperformed by GraphRTA in most cases.
We observe that the H-score provides a more comprehensive evaluation metric than accuracy. Sev- eral baselines achieve high accuracy scores but suffer from low H-scores, reflecting their difficulty in accurately identifying open-set instances. For example, while Ope achieves an accuracy of 49.98% in the scenario of CCMvv-,Citations limitations in open-set recognition. Additionally, most models struggle with heterophilous datasets like ogbn-arxiv and WebKB, which pose additional challenges due to their weakly correlated char- acteristics. Two recent baselines encounter issues in this context, for instance, G2Pxy fails to meet
its H-score only about 5.57%, illustrating its
8

---

Table 4: Classification H-score with different known classes.
Classes
A->C
A-+D
C->A
C-+D
D-+A
D->C
Okwn 2 53.39 45.29-13.46
41.10+2.63 41.54412.92
44.48112.86 54.1844.411
Cknn =3 3 66.33#1.69 64.42+1.10
62.89=2.46 65.99=187 59.41=2.22 62.33++1.3
Okwn 4 65.74:50.54 63.28:0069 61.63=0064 67.20=0.63 57.12:10.35 59.82:10.69
Table Classification H-score with different openset class detection strategies.
Methods
A-+C
A-D
C-A
C-+D
D
D-CC
58.8810.72 59.73=1.10 GraphRTA 66.3311.69 64.42 1.10 62.8912.46 1.89 59.41 1.53
GraphRTAshees
63.07-1..13 1.771 61.00 63,8911.35
the predefined rules for node embedding mixup, while SDA struggles form meaningful clusters under these conditions. In contrast, GraphRTA overcomes these limitations by leveraging model and graph reprogramming, enabling it to deliver robust performance without such constraints.
4.3 Ablation Studies
Impact of Different Known Classes. We have also conducted additional ablation studies to eval- uate the impact of varying the number of known classes on the model's 's performance. Specifically, we aim to understand how the size of the known class subset influences the model's ability to dis- criminate between known and unknown categories. As the number of known classes increases, the boundary between known and unknown nodes becomes more complex, potentially affecting the generalization capability of the domain adaptation process. The results, reported in terms of the H-score on the citation dataset, are presented in Table 4. When the number of known classes is very small (e.g., only 2 known classes), open-set models have limited supervision to guide effective feature alignment and decision boundary formation, leading to less stable performance. In contrast, as more known classes are introduced, the model benefits from richer supervision, enabling more discriminative representations and improving robustness against the presence of unknown classes. These observations highlight that our model becomes progressively more reliable and less sensitive to open-set uncertainty as the number of known classes increases.
Alternative Open-set Detection Strategies. We further implemented an entropy-based threshold- ing variant, which replaces the 3-class classification scheme with confidence-basee decision rule. Specifically, samples with prediction entropy exceeding pre-defined threshold are regarded as be- longing to unknown classes. We then compared this variant with our proposed approach across all experimental settings. The results, measured by the H-score Table 5, show that our method con- sistently outperforms the entropy-based thresholding strategy, demonstrating its superior ability to distinguish between known and unknown nodes in the open-set graph domain adaptation scenario.
Impact of Different GNN Architectures. As discussed in Section 3.2, our proposed GraphRTA is designed to be compatible with a range of GNN architectures, allow-
Table 6: H-scores with different GNN architectures.
Architectures
D CC
D >A
A >C
ing flexibility across models. We examine its performance using prominent GNN frameworks: GCN [25], GraphSAGE [14], and GAT [48], with results presented in Ta- ble 6. These results indicate that while all architectures benefit from the proposed dual reprogram-
GraphRTAGcN
62.33-+1.53
59.41+2.22
66.33+11.9
GraphRTAssco
59.09:t2.66
$5.45:11.49
13.11
GraphRTAGAT
54.17#6.60
51.82+9.14
56.51
ming approach, their performance varies across different datasets, highlighting the influence of ar- chitectural design on adaptation capabilities. Among them, GAT achieves the lowest performance, which adapt poorly from the source to target graphs due to its attention mechanism. The multi- head attention paradigm also requires substantial parameter tuning, which may hinder adaptation. Interestingly, GCN, despite its simpler design, consistently performs well, demonstrating resilience across most tasks.
Visualization. better understand the quality of the learned node representations, we use t- SNE [47] to project them into a 2-D space and visualize the results in Figure 2 through scatter plots. More specifically, the three known classes are represented by red, blue, and green, while the unknown class is depicted in orange. The vanilla GCN [25], without any adaptation, struggles to

---

S3
(a) GCN
(b) DANCE
(c) SDA
(d) GraphRTA
Figure 2: Visualization of node representations the target graph for the citation dataset (A-C) with the unknown class highlighted in orange.
produce distinct clusters, leading to significant overlap between nodes of the unknown class and those of the known classes. This overlap occurs because the model lacks mechanisms to address distribution shifts between the source and target graphs. In contrast, two representative open-set baselines, DANCE [40] and SDA [49], are able to identify nodes from the target unknown class, but their boundaries are blurred, with most nodes from unknown classes often blending into known class clusters. Our proposed GraphRTA achieves relatively clearer separation, generating compact clusters for known classes while effectively isolating open-set instances.
Syyper-paramtee Sensitivity. In this section, we analyze the effects of two important hyperparameters: the sparsity ratio within the model re- programming module and the struc- tural modification ratio B in the graph reprogramming module. As illus- trated in Figure 3, the sparsity ratio significantly influences the model's performance; as the masking ratio increases, the performance declines sharply. This impairs the model's ability to capture essential patterns due excessive weight masking. In contrast, the performance impact of adjusting the graph structure change ratio is relatively robust, suggesting that the model can adapt moderate structural alterations in the graph without significant degradation. Additional analyses of other hyperparameters and representation visualizations are provided in the Appendix B.
70
A-1
D-C
Medel quest
Glagle duoege
(a) Model reprogramming ratio
(b) Graph reprogramming ratio
Figure 3: H-scores under different ratios reprogramming.
5 Conclusion
This paper studies unsupervised open-set graph domain adaptation, under-explored area in the graph community, where the target graph introduces new classes that are not present in the source graph. To address the source bias and distributional shift problems, we propose novel frame- work named GraphRTA that conducts dual reprogramming at the model as well as the graph levels. Through extensive evaluations GraphRTA consistently outperforms or matches the performance recent state-of-the- thetthe-rt models. In future work, we aim to extend our framework address additional challenges in graph adapta- tion, such as source-free open-set graph domain adaptation, semi-supervised open-set graph domain adaptation, and outto--diitribution detection, thereby broadening its applicability and enhancing its robustness as well as generalization in real-world scenarios.
variety of public datasets, further show that proposed
6 Border Impacts and Limitations
This paper advances the field of machine learning, particularly in Open-Set Graph Domain Adap- tation, which enables graph models to adapt to new, unseen classes across domains. It has broad applications, including fraud detection, biological network analysis, and recommendation systems. While our work improves model robustness and generalization, potential societal impacts include both benefits (e.g.. better adaptability in real-world graph-based systems) and challenges (e.g., risks of biased adaptation or misclassification in high-stakes applications). However, we do not identify any immediate specific societal concerns.
10

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Acknowledgments and Disclosure of Funding
This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative. Any opinions, findings and conclu- sions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Authority.
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70
70
DOC
60
DSA
A-C
Accuracy
Accuracy
60
40
D-CC
30
D-A
ASC
20
50
2
3
16
32
64
128
256
Number layers
Node representation dimension
(a) Number of layers
(b) Node representation dimension
Figure 4: Accuracy under different layers and dimension.
Table 7: Classification H-score with different componnnts.
Architectures
D-C
D-A
A-+C
GraphRTA, w/o MR
59.86=+0.38
56.31=0.68
64.93:0.24
GraphRTA w/o GR
60.34.10.62
56.711:1.29
62.95.11.97
GraphITAA
62.33
56.91
6..33.1..9
A Running Environment
Our experiments conducted Linux server with 2 AMD EPYC 7543 CPU@2.80GHz, 512G RAM and one NVIDIA A A100-SXM4-80GB GPU. The proposed model is implemented with Pytorch 1.13.1 in Python 3.8 using Pytorch Geometric 2.4.0.
B Additional Ablation Studies and Analyses
In this section, we conduct a series ablation studies to comprehensively assess the effectiveness of our proposed GraphRTA framework.
Sensitivity to Two Key Hyper-Parameters. We begin by analyzing the model's sensitivity to two critical pyper-parameters: the number of layers L and the node representation dimension d. As shown in Figure 4, model performance initially improves with an increase in the number layers but starts degrade beyond certain point (i.e., 2 layers). This decline is attributed overfitting, as deeper layers may overly adapt to the training data while failing to generalize effectively. In contrast, the model exhibits consistent robustness variations in the node representation dimension, highlighting its ability to perform well across a range of dimensional configurations.
Effectiveness of Model and Graph Reprogramming. Next, we further investigate the individual contributions of the model reprogramming (i.c., MR) and graph reprogramming (i.e., GR) compo- nents. Results presented in Table 7 provide insights into the impact of excluding these components. Specifically, the configuration labeled as "w/o MR" excludes the model reprogramming module, while "w/o GR" omits the graph reprogramming module. A configuration without either component demonstrates the most significant performance degradation. underscoring the necessity of incorpo- rating both modules. These findings highlight that the dual reprogramming strategy critical for mitigating domain shifts and improving model adaptability.
Table 8: H-score with different graph reprogramming strategies.
Architectures
D->C
D-+A
GraphRTA w SUBLIME (31)
59.72.11.03
54.2811148
GraphRTA, SLAPS (7)
60.65110.5
55.4711..2
GraphRTA
62.33+1..5
56.91+2.50
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Table MMD comparison before and after graph reprogramming
Methods
A--C
A->D
C->A
C-+D
D->A
D-+C
Before
0.0381
0,0402
0.0368
0.0399
0.0371
0.0378
After
0.3520
0.3932
0.5081
0.4682
0.3608
0.4477
Table 10: LLM integration for open-set detection.
Methods
Arxiv-I-AArii-II
Arxi--I-Arrii-III
Arxiv-II-+Arxiv
A2GNN
45.00-H0.24
43.1440.18
45.18-+0.17
A2GNN + LLLM-xxplanations
46.40:10.45
43.34:10.18
48.8110049
SDA SDA + LLM-expllnattios
42.60:10.15
39.44:10.21
46.03:10.18
44.10+11.75
40.55+0.10
48.50+0..9
GraphRTA
50.79.12.79
46,25
48.42.11.90
GraphRTA LLM-explanations
51.56.11.23
47.5910.48
50.18.10.79
Comparison with Alternative Graph Reprogramming Strategies. We also evaluate two alter- native graph reprogramming approaches, SUBLIME [31] and SLAPS [7], both of which employ self-supervised learning techniques. The results, summarized in Table 8, reveal that our approach outperforms these strategies. SUBLIME relies on GNN- based node similarity learning followed by KNN-based sparsification to generate a sparse adjacency matrix. This dependence on nearest neighbors limits flexibility and performance. SLAPS, on the other hand, employs a denoising au- toencoder loss for graph reconstruction, which introduces constraints that may not generalize well across different datasets. By avoiding such dependencies, our method achieves superior versatility and effectiveness, demonstrated in the experiments.
How Does Graph Reprogramming Affect the Feature Distribution of Unknown Classes? We measure the distributional divergence between known and unknown class features using the Maxi- mum Mean Discrepancy (MMD) metric. This analysis provides quantitative assessment of whether our approach enhances the separability of known and unknown samples in the latent space. The re- sults shown in Table indicate that graph reprogramming significantly increases the distributional distance between the two groups after adaptation, suggesting that it effectively promotes clearer feature separation and mitigates overlap between known and unknown domains.
C Potential of LLM Integration for Unknown Class Detection
Integrating large language models (LLMs) into open-set graph domain adaptation could offer promising opportunities, particularly for datasets where nodes are associated with rich textual in- formation. To explore this direction, we investigate the impact of LLM--genrrted semantic expla- nations on unknown class detection. Specifically, we adopt the prompt strategy from TAPE [15] to generate semantic explanations for each node in the ogbn-arxiv dataset. For each node, we con- struct textual features by concatenating the title, abstract, and the corresponding ALM--generated explanation, thereby enriching the semantic content available to the model.
We then evaluate the effectiveness of these enhanced node features using three representative open- set graph adaptation models. Each experiment is conducted five times with different random seeds, and we report the average H-score along with the standard deviation ensure statistical reliability, The results, summarized in Table 10, show that incorporating LLM-ggnnrated explanations consis- tently improves performance across all evaluated models. This improvement highlights that LLMs can provide complementary semantic signals that help distinguish between known and unknown classes, thereby enhancing the robustness of open-set graph domain adaptation. Overall, these find- ings suggest that leveraging J..M-generated semantic knowledge can substantially benefit open-set scenarios, especially in text-rich graph datasets. We consider this a promising direction for future research.
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D Complexity Analysis
Let Gs denote the source graph consisting n, nodes and e, edges, G, represent the target graph with nodes and e, edges. Assume that the node representation dimension is d and the graph neural network has L layers. Then, the time complexity associated with encoding the feature repre- sentations of both the source and target graphs is given by ( (n, + n4) + Ld e, + ez)). For the model reprogramming phase, the complexity of sorting the gradient magnitudes O (Ldlog(d)) In the context of graph reprogramming, the complexity for transforming node features is (n,,d), while the refinement of the graph structure takes a complexity of ((t)). Furthermore, additional computa- tion arises from the posterior inference process, which is characterized by complexity of On,)", where denotes the number iterations the Expectation- Maximization (EM) procedure, as- sumed to be constant. Thus, the overall computational complexity of the proposed framework falls within the same order magnitude as existing methods.
Finally, to enhance clarity about our training procedure, we present comprehensive step-by-step outline Algorithm 1.
Algorithm 1 GraphRTA's Training Strategy
1: Input: Given labeled source graph G. and unlabeled target graph Gr, graph neural network
4 fw 9o
2: Output: Target graph predictions Y, E [2x
3: Randomly initialize weights of fu and Pe
4: while not reached the maximum epochs do
5: for batch data from source and target graph do
6: Fix the parameters of graph reprogramming
7: Conduct model reprogramming with Eq.
8:
end for
9:
Update model as reprogrammed GNN for batch data from source and target graph do
10:
Fix the parameters of model reprogramming
12:
Conduct graph reprogramming with Eq. 4) end for
14:
Update target graph as reprogrammed graph
15:
end while
16: Compute Y, pucx(c.1+1 with graph neural network
24


Reviews:
[{"review_id": "liPs1CTa3Z", "reviewer": "Reviewer_s6Bn", "summary": "This paper addresses the task of unsupervised open-set graph domain adaptation, where the target domain may contain classes do not present in the source domain, an important consideration in real-world applications. To tackle this challenge, the authors propose a novel framework that simultaneously classifies target nodes into known categories while identifying unseen node types as belonging to an unknown class. The approach involves reprogramming at both the model and graph levels. Specifically, model reprogramming is achieved by pruning domain-specific parameters to mitigate bias, while graph reprogramming adjusts graph structures to enhance the separation between known and unknown classes. Experimental results on multiple public datasets demonstrate that the proposed method outperforms recent baselines by varying margins.", "questions": "1. The authors should discuss and compare with the following AAAI 2025 paper [1].\n[1] Shen X, Chen Z, Pan S, et al. Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment, Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(19): 20398-20408.\n2. Incorporating further ablation studies could enhance the paper. Specifically, the effect of varying the number of known classes—from 2 up to 4—on the model’s performance remains unexplored. Investigating this aspect would provide valuable insight into the model’s robustness under different complexities.\n3. The organization of the paper could be further improved by simplifying Section 3.5. Streamlining this section would enhance clarity and help readers follow the key concepts more easily, making the overall presentation more coherent and accessible.\n4. How does graph reprogramming affect the feature distribution of unknown classes? A detailed analysis of this effect would help clarify whether the approach effectively enhances the distinction between known and unknown classes.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "# Strengths:\n1.\tThis paper is well written and easy to understand.\n2.\tThis paper investigates an important and interesting problem, i.e., open-set graph domain adaptation.\n3.\tThe model’s performance is pretty good compared with existing methods.\n# Weaknesses:\n1.\tThe authors fail to discuss and compare with a recent relevant baseline.\n2.\tAdditional ablation studies would strengthen the paper. For instance, it remains unclear how varying the number of known classes impacts the model’s performance.\n3.\tThe description of the training procedure is too long, which could be simplified.", "was_revised": true, "final_justification": "The score I have provided accurately reflects my current positive evaluation of the paper, and I will therefore maintain my current score.", "rebuttal": "We greatly appreciate Reviewer s6Bn for the insightful review to help us improve the paper. We address the reviewer’s concerns as follows:\n\n***\n\n*Q1. The authors should discuss and compare with the following AAAI 2025 paper.*\n\nA1. Thank you for bringing this work to our attention. The UAGA method you mentioned is a two-stage framework. It first separates known and unknown classes by training a graph neural network, and then it applies unknown-excluded adversarial domain alignment across different classes. Notably, these operations are performed entirely in the representation space. In contrast, our proposed GraphRTA introduces model reprogramming and graph reprogramming from dual perspectives, which enhances generalization capabilities by explicitly modeling the model space and the graph space. Following your suggestion, we have compared UAGA with GraphRTA on citation datasets. The results of H-score are as follows:\n\n| Methods | A->C | A->D | C->A | C->D | D->A | D->C |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| UAGA | 61.34$\\pm$1.16 | **67.50$\\pm$2.95** | 60.59$\\pm$5.95 |64.81$\\pm$4.50 | 55.73$\\pm$3.98 | 52.16$\\pm$2.18 |\n| GraphRTA | **66.33$\\pm$1.69** | 64.42$\\pm$1.10 | **62.89$\\pm$2.46** | **65.99$\\pm$1.87** | **59.41$\\pm$2.22** | **62.33$\\pm$1.53** |\n\nAs can be seen, our method demonstrates superior performance over the baseline in 5 out of 6 evaluated scenarios, indicating strong generalization and robustness.\n\n***\n\n*Q2. The effect of varying the number of known classes—from 2 up to 4—on the model’s performance remains unexplored.*\n\nA2. Thanks for this suggestion. Following your suggestion, we have conducted additional ablation studies to evaluate the impact of varying the number of known classes (from 2 to 4) on model performance. The results of H-score on citation dataset are presented below:\n\n| Methods | A->C | A->D | C->A | C->D | D->A | D->C |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| $C_{kwn}$=2 | 53.39$\\pm$1.87 | 45.29$\\pm$3.46 | 41.10$\\pm$2.63 | 41.54$\\pm$2.92 | 44.48$\\pm$2.86 | 54.18$\\pm$4.41 |\n| $C_{kwn}$=3 | 66.33$\\pm$1.69 | 64.42$\\pm$1.10 | 62.89$\\pm$2.46 | 65.99$\\pm$1.87 | 59.41$\\pm$2.22 | 62.33$\\pm$1.53 |\n| $C_{kwn}$=4 | 65.74$\\pm$0.54 | 63.28$\\pm$0.69 | 61.63$\\pm$0.64 | 67.20$\\pm$0.63 | 57.12$\\pm$0.35 | 59.82$\\pm$0.69 |\n\nWhen the number of known classes is very small (e.g., only 2 known classes), open-set models have less supervision to guide feature alignment and separation. In contrast, when enough known classes is available, our model becomes more robust and less sensitive is to the number of unknown classes.\n\n***\n\n*Q3. The organization of the paper could be further improved by simplifying Section 3.5.*\n\nA3. Thank you for the constructive feedback regarding the organization of the paper. We agree that simplifying Section 3.5 would improve clarity and overall coherence. We will revise and streamline this section in the final version to enhance readability and make the key concepts more accessible.\n\n***\n\n*Q4. How does graph reprogramming affect the feature distribution of unknown classes?* \n\nA4. Thank you for your insightful comment. To analyze how graph reprogramming affects the feature distribution of unknown classes, we measure the distributional divergence between known and unknown classes using Maximum Mean Discrepancy (MMD). This allows us to quantitatively assess whether our approach enhances the separation between these two groups. The results are as follows:\n\n| Methods | A->C | A->D | C->A | C->D | D->A | D->C |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| Before | 0.0381 | 0.0402 | 0.0368 | 0.0399 | 0.0371 | 0.0378 |\n| After | 0.3520 | 0.3932 | 0.5081 | 0.4682 | 0.3608 | 0.4477 |\n\nThe results indicate that graph reprogramming increases the distributional distance between known and unknown classes after graph reprogramming, suggesting improved feature separation. We will include this analysis in the final version of the paper."}, {"review_id": "GTyG5akDpN", "reviewer": "Reviewer_bwmJ", "summary": "This paper addresses the problem of unsupervised open-set graph domain adaptation (UOSGDA), where the target graph contains classes unseen in the source domain. The authors propose GraphRTA, a dual-reprogramming framework that modifies both graph structure/node features (\"graph reprogramming\") and prunes domain-specific model parameters (\"model reprogramming\"). Key innovations include: (1) Joint graph and model adaptation to separate known/unknown classes; (2) An extended classifier with an additional \"unknown\" dimension to avoid manual thresholding. Experiments on public datasets demonstrate superiority over GNNs, closed-set GDA, and open-set graph learning baselines.", "questions": "1.Potential of LLM Integration: Given datasets with text-describable nodes (e.g., Citation networks), have you considered leveraging LLMs for unknown class detection? LLMs could generate semantic explanations for classification decisions and handle the open-set classification.\n\n2.There is an inconsistency in reported baselines: Table 4 cites GCN HS metric as 56.91%, while Table 2 lists as 59.41%. Please verify and correct all numerical results.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality:\nStrength: Experimental design is rigorous, covering diverse baseline types (GNNs, closed-set GDA, open-set methods) across multiple datasets.\nWeakness: Model complexity raises concerns. The combination of adversarial training, multiple loss functions, and dual reprogramming may lead to training instability and convergence difficulties.\n\nClarity:\nStrength: The manuscript is well-structured and clearly written, with logically motivated components.\n\nSignificance and Originality:\nWeakness: While UOSGDA is a relevant problem, the technical novelty is limited. Core techniques (entropy-based known/unknown separation, adversarial domain alignment) are adaptations of existing methods from non-graph open-set domains. The incremental contribution does not sufficiently advance the state-of-the-art.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer bwmJ for the constructive feedback. We address the reviewer’s concerns as follows.\n\n***\n\n*Q1. Model complexity raises concerns. The combination of adversarial training, multiple loss functions, and dual reprogramming may lead to training instability and convergence difficulties.*\n\nA1. Thank you for raising this point. For model complexity, we have provided a detailed model complexity analysis in the **Appendix D**, which shows that the overall computational overhead remains manageable and comparable to existing domain adaptation methods. For training instability, we mitigate potential instability by using the gradient reversal layer (GRL), a well-established technique in adversarial training that enables stable joint optimization without requiring alternating updates. In our experiments, we did not observe instability or convergence issues during training across all datasets. We will include additional training details and convergence plots in the final version to demonstrate the stability of our method.\n\n***\n\n*Q2. Core techniques (entropy-based known/unknown separation, adversarial domain alignment) are adaptations of existing methods from non-graph open-set domains. The incremental contribution does not sufficiently advance the state-of-the-art.*\n\nA2. Thank you for your feedback. We believe our proposed dual reprogramming provides a new and meaningful direction for open-set graph domain adaptation that goes beyond adapting existing ideas. By reprogramming the model and the graph from dual perspectives, the method effectively reshapes the learning objective to focus on domain-invariant and known-class-relevant information. This is a novel strategy that contributes to the method’s strong performance under open-set conditions. To summarize, we would like to emphasize our contributions as follows: \n\n(1) For **problem**, we explore the challenging yet practical problem of unsupervised open-set graph domain adaptation, which remains relatively uninvestigated within the graph community.\n(2) For **model**, we propose a dual reprogramming framework named GraphRTA that jointly reprograms the model and the graph structure to better handle the open-set setting in graphs. This is fundamentally different from conventional methods that operate solely in the representation space. \n(3) For **evaluation**, we conduct extensive comparisons with 14 baselines to validate the effectiveness of our proposed GraphRTA. The baselines encompass various categories, such as no-adaptation methods, closed-set graph domain adaptation, and open-set graph domain adaptation methods.\n\nWe believe these contributions offer a substantive advancement in the field of open-set graph domain adaptation.\n\n***\n\n*Q3. Potential of LLM Integration: Given datasets with text-describable nodes (e.g., Citation networks), have you considered leveraging LLMs for unknown class detection? LLMs could generate semantic explanations for classification decisions and handle the open-set classification.*\n\nA3. Thank you for the insightful suggestion. We agree that integrating large language models (LLMs) into open-set graph domain adaptation holds great potential, especially for datasets with text-describable nodes. To explore the impact of LLM generated semantic explanations on unknown class detection, we follow the prompt strategy from TAPE [1] to generate semantic explanations for each node in the **ogbn-arxiv dataset**. The node features are then constructed by combining the title, abstract, and the LLM-generated explanation. We evaluate the effectiveness of these enhanced features using three models. Each experiment is run 5 times, and we report the average H-score along with the standard deviation as follows:\n\n| Methods | Arxiv I->Arxiv II | Arxiv I->Arxiv III | Arxiv II->Arxiv III |\n|:--:|:--:|:--:|:--:|\n| A2GNN | 45.00$\\pm$0.24 | 43.14$\\pm$0.18 | 45.18$\\pm$0.17 |\n| A2GNN + LLM-explanations | **46.40$\\pm$0.45** | **43.34$\\pm$0.18** | **48.81$\\pm$0.49** |\n| SDA | 42.60$\\pm$0.15 | 39.44$\\pm$0.21 | 46.03$\\pm$0.18 |\n| SDA + LLM-explanations | **44.10$\\pm$1.75** | **40.55$\\pm$0.10** | **48.50$\\pm$0.69** |\n| GraphRTA | 50.79$\\pm$2.79 | 46.25$\\pm$0.40 | 48.42$\\pm$1.94 |\n| GraphRTA + LLM-explanations | **51.56$\\pm$1.23** | **47.59$\\pm$0.48** | **50.18$\\pm$0.79** |\n\nAs shown in the table, incorporating LLM-generated explanations leads to performance improvements across the evaluated models. This suggests that LLMs can enhance the models and provide additional semantic signals beneficial for open-set classification. We consider this a promising direction for future research and will include a brief discussion of this potential extension in the final version of the paper.\n\nReference:\n\n[1] He X, Bresson X, Laurent T, et al. Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning[C]// ICLR 2024.\n\n***\n\n*Q4. There is an inconsistency in reported baselines: Table 4 cites GCN HS metric as 56.91%, while Table 2 lists as 59.41%. Please verify and correct all numerical results.*\n\nA4. Thank you for pointing this out. We have carefully reviewed all numerical results and confirm that the discrepancy is due to a typo in Table 4, where the accuracy value (56.91%) was mistakenly placed in the H-score for GCN backbone. The correct H-score is 59.41%, as reported in Table 2. We have verified all other results, and this is the only such error. The correction will be made in the final version."}, {"review_id": "Pa6nd5UriN", "reviewer": "Reviewer_ny1J", "summary": "This paper studies unsupervised open-set graph domain adaptation. The authors claim that the target domain might include classes not in the source domain in the real-world scenarios. To address this issue, the authors propose to not only classify target nodes into the known classes but also classify unseen node types into the unknown class. Specifically, a novel framework is proposed to conduct reprogramming on both the graph and model sides. For model reprogramming, the authors prune domain specific parameters to reduce bias. For graph reprogramming, the authors reprogram the graphs to facilitate better separation of known and unknown classes. Results on several public datasets show that the proposed model can outperform recent baselines with different gains.", "questions": "1.\tThe authors should provide a more detailed introduction to the datasets used in the experiments. For example, it would be helpful to clarify how open-set classes are selected within each dataset.\n2.\tOne of the recent baselines appears to be missing from the discussion and experimental comparison. Including this baseline would provide a more complete and fair evaluation of the proposed approach. Please refer to reference [1].\n3.\tAdditional ablation studies would strengthen the paper by providing deeper insights into the effectiveness of the proposed method. For example, it would be helpful to explore alternative strategies for open-set detection—such as using an entropy-based threshold—and compare their performance with the proposed approach.\n4.\tSome of the ablation studies currently placed in the appendix are crucial for understanding the core contributions of the paper and should be moved to the main content.\nReferences:\n[1] Shen X, Chen Z, Pan S, et al. Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(19): 20398-20408.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Pros:\n1.\tThis paper investigates open-set graph domain adaptation, which is interesting and less explored in the community.\n2.\tDifferent types of datasets (citation, ogbn-arxiv and WebKB) are utilized for evaluation.\n3.\tComprehensive results demonstrate that its performance is promising.\nCons:\n1.\tThe authors should provide a more detailed introduction to the datasets.\n2.\tOne of the recent baselines is not discussed and compared.\n3.\tMore ablation studies should be given. What if we use entropy threshold for open-set detection?", "was_revised": true, "final_justification": "I appreciate the rebuttal, and it addresses my concerns regarding the usefulness of the experiment results. Given these considerations, I will raise 4 points to 5. After seeing the discussion in the rebuttal, I strongly support accepting this paper.", "rebuttal": "We greatly appreciate Reviewer ny1J for the insightful review to help us improve the paper. We address the reviewer’s concerns as follows:\n\n***\n\n*Q1. The authors should provide a more detailed introduction to the datasets used in the experiments. For example, it would be helpful to clarify how open-set classes are selected within each dataset.*\n\nA1. Thank you for your suggestion. In our experiments, we follow the common practice used in prior works such as SDA and G2Pxy by selecting the first several classes (based on label indices) as known classes, and the remaining classes as open-set (unknown) classes. We will provide a more detailed description of this setup in the final version of the paper to improve clarity.\n\n***\n\n*Q2. One of the recent baselines appears to be missing from the discussion and experimental comparison.*\n\nA2. Thank you for bringing this work to our attention. The UAGA method you mentioned is a two-stage framework. It first separates known and unknown classes by training a graph neural network, and then it applies unknown-excluded adversarial domain alignment across different classes. Notably, these operations are performed entirely in the representation space. In contrast, our proposed GraphRTA introduces model reprogramming and graph reprogramming from dual perspectives, which enhances generalization capabilities by explicitly modeling the model space and the graph space. Following your suggestion, we have compared UAGA with GraphRTA on citation datasets. The results of H-score are as follows:\n\n| Methods | A->C | A->D | C->A | C->D | D->A | D->C |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| UAGA | 61.34$\\pm$1.16 | **67.50$\\pm$2.95** | 60.59$\\pm$5.95 |64.81$\\pm$4.50 | 55.73$\\pm$3.98 | 52.16$\\pm$2.18 |\n| GraphRTA | **66.33$\\pm$1.69** | 64.42$\\pm$1.10 | **62.89$\\pm$2.46** | **65.99$\\pm$1.87** | **59.41$\\pm$2.22** | **62.33$\\pm$1.53** |\n\nAs can be seen, our method demonstrates superior performance over the baseline in 5 out of 6 evaluated scenarios, indicating strong generalization and robustness.\n\n***\n\n*Q3. It would be helpful to explore alternative strategies for open-set detection—such as using an entropy-based threshold—and compare their performance with the proposed approach.*\n\nA3. Thank you for the insightful suggestion. We agree that exploring alternative open-set detection strategies provides valuable perspective. Based on your suggestion, we implemented an entropy-based thresholding variant and compared its performance with our proposed approach. The results of H-score show that our approach consistently outperforms it across all settings. \n\n| Methods | A->C | A->D | C->A | C->D | D->A | D->C |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| GraphRTA-threshold | 63.07$\\pm$0.13 | 61.21$\\pm$1.07 | 61.00$\\pm$1.52 |63.89$\\pm$1.35 | 58.88$\\pm$0.72 | 59.73$\\pm$1.10 |\n| GraphRTA | **66.33$\\pm$1.69** | **64.42$\\pm$1.10** | **62.89$\\pm$2.46** | **65.99$\\pm$1.87** | **59.41$\\pm$2.22** | **62.33$\\pm$1.53** |\n\nWe will include these results and discussion in the final version of the paper to provide a more comprehensive evaluation.\n\n***\n\n*Q4. Some of the ablation studies currently placed in the appendix are crucial for understanding the core contributions of the paper and should be moved to the main content.*\n\nA4. Thank you for your helpful feedback. We agree that some of the ablation studies (e.g., Table 5 and Table 6) are important for understanding the core contributions of our work. In the final version of the paper, we will move these ablation results from the appendix to the main text to improve the clarity of our contributions."}, {"review_id": "d7X3lnqxDZ", "reviewer": "Reviewer_2LyR", "summary": "This paper addresses the problem of unsupervised open-set graph domain adaptation, where the goal is not only to correctly classify target nodes belonging to known classes but also to detect previously unseen node types as unknown. The authors propose a framework called GraphRTA, which performs reprogramming on both the graph and model levels. Specifically, they modify the target graph structure and node features to improve the separation of known and unknown classes. Domain-specific parameters are pruned to reduce bias toward the source graph while retaining transferable patterns. The classifier is extended with an additional dimension to represent the unknown class.", "questions": "Q1: How is the value of the budget B determined?\n\nQ2: How is the learning of \\delta A achieved in practice?\n\nAdditionally, my current evaluation assumes that the authors will successfully address the concerns and confusions mentioned in the weaknesses section, so I would appreciate it if the authors could also respond to the questions raised there.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper proposes a new solution for open-set domain adaptation (OSDA) in graph neural networks, explicitly distinguishing between known and unknown node classes.\n\n2. The overall writing is generally clear and fluent, and the algorithm is logically presented.\n\n3. Experimental results demonstrate improved performance compared to existing methods.\n\n\nWeaknesses:\n\n1. Some parts of the paper still remain unclear, which makes it difficult to fully understand the core ideas. For example, in the second paragraph of Section 3.3, the use of M as a mask in the node representation is not well explained. It appears to be a learnable parameter, yet the following text mentions that weights with small gradients with respect to M are masked. This suggests M might instead be derived from another process. The explanation needs clarification.\n\n2. In the same section, the hyperparameter \\rho is mentioned, but its definition and role in the model are not clearly described. Additionally, a dynamic adaptive threshold is referenced but not sufficiently explained. Since this is claimed as a key contribution, it should be fully justified and elaborated.\n\n3. On line 200, it is unclear how \\delta A is computed. If it is a learnable parameter, how is the binary matrix learned? Also, the choice of the budget B is not discussed. This seems like an important hyperparameter that could significantly impact performance, so its value, how to choose, and default setting should be explained in more detail.\n\n4. The paper employs a Beta mixture model, which appears nearly identical to the one proposed in \"Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation\". Although the authors cite this work in the introductory description at the beginning of the paragraph, they should explicitly acknowledge the similarity in this section, as the techniques are almost the same.\n\n5. While the proposed method shows strong performance in most scenarios in Tables 2 and 3, it would be helpful to discuss the cases where the method does not achieve the best results, and explore possible reasons why.", "was_revised": true, "final_justification": "The authors’ rebuttal addressed most of my initial concerns, particularly regarding methodological details and hyperparameter configurations, and provided helpful clarifications. These clarifications should be incorporated into the paper to improve clarity for a broader audience. I also note that the Beta mixture model, a key component of the method, closely resembles techniques in prior work. The paper should more explicitly acknowledge related literature and clarify the originality of this component to avoid potential misinterpretation. Some reviewer has raised concerns about the technical novelty, given that the core techniques are adaptations of existing methods from non-graph open-set domains. While I am familiar with non-graph DA techniques, I am less familiar with graph-based DA approaches and will therefore take a conservative stance regarding the novelty of this work. Taking all these factors into account, I will maintain my rating and confidence in my review.", "rebuttal": "We sincerely thank the Reviewer 2LyR for the thorough and insightful feedback. Our detailed responses to your concerns are listed as follows:\n\n***\n\n*Q1. In the second paragraph of Section 3.3, the use of $M$ as a mask in the node representation is not well explained. The explanation needs clarification.*\n\nA1. Thank you for your helpful feedback. In our method, $M$ is a learnable parameter, initialized randomly and updated during training. It is applied as a soft mask on graph neural network weights to enable domain invariant feature learning during model reprogramming. The sentence referencing weights with small gradients with respect to $M$ being masked was intended to illustrate how uninformative components are implicitly suppressed during training via gradient-based optimization, not that $M$ is derived from another process. We will revise this paragraph in the final version to make the role of $M$ as a learnable soft mask more explicit and remove any misleading phrasing that may have caused confusion.\n\n***\n\n*Q2. In the same section, the hyperparameter $\\rho$ is mentioned, but its definition and role in the model are not clearly described. Additionally, a dynamic adaptive threshold is referenced but not sufficiently explained. Since this is claimed as a key contribution, it should be fully justified and elaborated.*\n\nA2. Thank you for pointing this out. The hyperparameter $\\rho$ is used to control the sparsity of the learned mask $M$ in the model reprogramming process. Specifically, it determines the proportion of weights to be retained, allowing the model to focus on the most informative components of the representation while suppressing less relevant ones. We set the lowest $\\rho$ percent of gradient values in $M$ to zero, leaving the remaining elements at one. These sparse masks are then applied to prune the weight matrix $W$, resulting in a reprogrammed sparse model that emphasizes informative regions of the feature space.\n\nFor dynamic threshold, we mean we adopt a logit augmentation strategy, where we append an additional logit dimension to represent the \"unknown\" class. This augmented logit vector is then passed through a softmax layer, allowing the model to assign a probability distribution over both known and unknown classes. The final prediction is simply the class with the highest posterior probability. The \"dynamic threshold\" referenced in the text refers not to an explicit numerical threshold, but to the model's ability to adaptively learn the decision boundary between known and unknown classes based on the input node representation $z$ and the augmented classifier outputs. This formulation removes the need for hand-tuned thresholds and makes the open-set detection process fully data-driven and end-to-end trainable. We will revise the wording in the final version to avoid ambiguity and better reflect this mechanism.\n\n***\n\n*Q3. On line 200, it is unclear how $\\Delta{A}$ is computed. If it is a learnable parameter, how is the binary matrix learned? Also, the choice of the budget $B$ is not discussed.* \n\nA3. Thank you for your thoughtful question. To maintain a discrete and sparse adjacency structure, we treat each entry in $\\Delta{A}$ as a Bernoulli random variable, where the probability of an edge being present is learned. During training, we sample the learned graph structure from these Bernoulli distributions, allowing the model to adaptively reprogram the input graph while keeping the structure binary. This formulation enables stochastic but learnable structure refinement in a principled manner.\n\nRegarding the budget $B$, it is used to constrain the total number of edges that can be added or modified in $A$, which helps control overfitting and keeps the reprogramming meaningful. In practice, we set $B$ as a fixed percentage (e.g., 20%) of the total possible edges and have provided an **ablation study in Figure 2(b)** to analyze its impact. We will add a more detailed explanation of this process, along with the default setting in the final version of the paper.\n\n***\n\n*Q4. The paper employs a Beta mixture model, which appears nearly identical to the one proposed.* \n\nA4. Thank you for your feedback. We agree that the Beta mixture model we adopt is closely aligned with the previous work. We would like to clarify that our primary contribution is not in designing a new loss function, but in introducing a novel learning paradigm, i.e., dual reprogramming for open-set graph domain adaptation, which integrates model and graph reprogramming to address challenges specific to graph-structured data. The Beta mixture model is used as a standard component within our framework to support open-set recognition, consistent with prior work.\n\n***\n\n*Q5. While the proposed method shows strong performance in most scenarios in Tables 2 and 3, it would be helpful to discuss the cases where the method does not achieve the best results and explore possible reasons why.*\n\nA5. Thank you for your insightful comment. We appreciate your suggestion to analyze the cases where our method does not achieve the best performance. Upon reviewing the results in Tables 2 and 3, we note that in a few scenarios, our method performs slightly below certain closed-set baselines. These cases often occur in settings where the known class distribution is very similar with unknown class distributions across the two domains. We compute the Maximum Mean Discrepancy (MMD) between known and unknown classes within each domain and then calculate their MMD gap between the source and target domains. We find that DBLPv7->ACMv9 (0.003) and Texas->Cornell (0.024) have the smallest values among all the scenarios. In this case, closed-set models leverage the full target space for alignment without needing to separate unknown classes, resulting in a performance improvement. We will include this analysis and a brief discussion of these observations in the final version to help readers better understand our approach.\n\n***\n\n*Q6. How is the value of the budget $B$ determined?*\n\nA6. Thanks for pointing this out. The budget $B$ is treated as a hyperparameter that controls the sparsity of the reprogrammed graph structure. In our experiments, we set $B$ to a fixed proportion (e.g., 20%) of the total possible edges. We further analyze the sensitivity of the model to different values of $B$ through an ablation study, as shown in Figure 2(b).\n\n***\n\n*Q7. How is the learning of $\\Delta{A}$ achieved in practice?*\n\nA7. Thanks for pointing this out. To learn $\\Delta A$ in practice, we treat each entry in $\\Delta A$ as a Bernoulli random variable, where the probability of an edge being present is learned. During training, we sample the learned graph structure from these Bernoulli distributions, allowing the model to adaptively reprogram the input graph while keeping the structure binary. The learning process is further constrained by a budget $B$ via projected gradient descent, which limits the number of added or modified edges. This formulation enables stochastic but learnable structure refinement in a principled manner. We will add a more detailed description in the final version."}]

Author rebuttal:

Meta-review: This paper aims to address the unsupervised open-set graph domain adaptation problem, where the goal is not only to correctly classify target nodes belonging to known classes but also to detect previously unseen node types as unknown. To this end, the authors  proposed a dual-reprogramming framework named GraphRTA, which modifies both graph structure/node features ("graph reprogramming") and prunes domain-specific model parameters ("model reprogramming"). Experiments on public datasets demonstrate superiority over GNNs, closed-set GDA, and open-set graph learning baselines.

Reviewers recognized the novelty and technical contributions of this work. This paper studies an important and challenging problem from a unique perspective. The idea of GraphRTA is well motivated and clearly explained. The extensive results also validated the effectiveness of the proposed approach. 

Meanwhile, reviewers raised some concerns about missing baselines, ablation studies, potential LLM integration, model complexity, etc. The authors have provided detailed responses with additional results, which have addressed most of these concerns from reviewers. The authors are strongly encouraged to incorporate the new results and discussions into the final version of the paper.
Decision: Accept (poster)