GenAI4ELab/papercli-papers huggingface dataset:
Highly Imperceptible Black-Box Graph Injection Attacks with Reinforcement Learning
Authors: Maochang Zhao; Jing Zhang
Venue: AAAI 2025 (main)

Abstract: Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial attacks. In practice, effectively attacking GNNs is not easy. Existing attack methods primarily focus on modifying the topology of the graph data. In many scenarios, attackers do not have the authority to manipulate the graph's topology, making such attacks challenging to execute. Although node injection attacks are more feasible than modifying the topology, current injection attacks rely on knowledge of the victim model's architecture. This dependency significantly degrades attack quality when there is inconsistency in the victim models. Moreover, the generation of injected nodes often lacks precise control over features, making it difficult to balance attack effectiveness and stealthiness. In this paper, we investigate a node injection attack under model-agnostic conditions and propose Targeted Evasion Attack via Node Injection (TEANI). Specifically, TEANI models the generation of adversarial nodes as a Markov process. Without considering the target model's structure, it guides the agent to select features that maximize attack effectiveness within a budget, based solely on the results of queries to a black-box model. Extensive experiments on real-world datasets and mainstream GNN models demonstrate that the proposed TEANI poses more effective and imperceptible threats than state-of-the-art attack methods.
Spatial-Temporal Knowledge Distillation for Takeaway Recommendation
Authors: Shuyuan Zhao; Wei Chen; Boyan Shi; Liyong Zhou; Shuohao Lin; Huaiyu Wan
Venue: AAAI 2025 (main)

Abstract: The takeaway recommendation system aims to recommend users' future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and boosting merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequences. However, two main challenges limit the performance of these approaches: (1) capturing dynamic user preferences on complex geospatial information and (2) efficiently integrating spatial-temporal knowledge from both graphs and sequence data with low computational costs. In this paper, we propose a novel spatial-temporal knowledge distillation model for takeaway recommendation (STKDRec) based on the two-stage training process. Specifically, during the first pre-training stage, a spatial-temporal knowledge graph (STKG) encoder is trained to extract high-order spatial-temporal dependencies and collaborative associations from the STKG. During the second spatial-temporal knowledge distillation (STKD) stage, a spatial-temporal Transformer (ST-Transformer) is employed to comprehensively model dynamic user preferences on various types of fine-grained geospatial information from a sequential perspective. Furthermore, the STKD strategy is introduced to transfer graph-based spatial-temporal knowledge to the ST-Transformer, facilitating the adaptive fusion of rich knowledge derived from both the STKG and sequence data while reducing computational overhead. Extensive experiments on three real-world datasets show that STKDRec significantly outperforms the state-of-the-art baselines.
Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling
Authors: Siqian Zhao; Sherry Sahebi
Venue: AAAI 2025 (main)

Abstract: Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections.
GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs
Authors: Songwei Zhao; Yuan Jiang; Zijing Zhang; Yang Yu; Hechang Chen
Venue: AAAI 2025 (main)

Abstract: Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Granular and Implicit Graph Network (GRAIN), a novel GNN model specifically designed for heterophilous graphs. GRAIN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, non-neighboring nodes. This approach effectively integrates local and global information, resulting in smoother, more accurate node representations. We also introduce an adaptive graph information aggregator that efficiently combines multi-granularity and implicit data, significantly improving node representation quality, as shown by experiments on 13 datasets covering varying homophily and heterophily. GRAIN consistently outperforms 12 state-of-the-art models, excelling on both homophilous and heterophilous graphs.
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
Authors: Yuhan Zhao; Rui Chen; Li Chen; Shuang Zhang; Qilong Han; Hongtao Song
Venue: AAAI 2025 (main)

Abstract: Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models rely on pairwise loss functions to approximate full rankings, resulting in an immense performance gap. In this paper, we provide a novel analysis using the multiple ordinal classification concept to reveal the inevitable gap between a pairwise approximation and the ideal case. However, bridging the gap in practice encounters two formidable challenges: (1) none of the real-world datasets contains full ranking information; (2) there does not exist a loss function that is capable of consuming ranking information. To overcome these challenges, we propose a pseudo-ranking paradigm (PRP) that addresses the lack of ranking information by introducing pseudo-rankings supervised by an original noise injection mechanism. Additionally, we put forward a new ranking loss function designed to handle ranking information effectively. To ensure our method's robustness against potential inaccuracies in pseudo-rankings, we equip the ranking loss function with a gradient-based confidence mechanism to detect and mitigate abnormal gradients. Extensive experiments on four real-world datasets demonstrate that PRP significantly outperforms state-of-the-art methods.
TRACI: A Data-centric Approach for Multi-Domain Generalization on Graphs
Authors: Yusheng Zhao; Changhu Wang; Xiao Luo; Junyu Luo; Wei Ju; Zhiping Xiao; Ming Zhang
Venue: AAAI 2025 (main)

Abstract: Graph neural networks (GNNs) have gained superior performance in graph-based prediction tasks with a variety of applications such as social analysis and drug discovery. Despite the remarkable progress, their performance often degrades on test graphs with distribution shifts. Existing domain adaptation methods rely on unlabeled test graphs during optimization, limiting their applicability to graphs in the wild. Towards this end, this paper studies the problem of multi-domain generalization on graphs, which utilizes multiple source graphs to learn a GNN with high performance on unseen target graphs. We propose a new approach named Topological Adversarial Learning with Prototypical Mixup (TRACI)  to solve the problem. The fundamental principle behind our TRACI is to produce virtual adversarial and mixed graph samples from a data-centric view. In particular, TRACI enhances GNN generalization by employing a gradient-ascent strategy that considers both label prediction entropy and graph topology to craft challenging adversarial samples. Additionally, it generates domain-agnostic node representations by characterizing class-graph pair prototypes through latent distributions and applying multi-sample prototypical Mixup for distribution alignment across graphs. We further provide theoretical analysis showing that TRACI reduces the model's excess risk. Extensive experiments on various benchmark datasets demonstrate that TRACI outperforms state-of-the-art baselines, validating its effectiveness.
Dynamic Spectral Graph Anomaly Detection
Authors: Jianbo Zheng; Chao Yang; Tairui Zhang; Longbing Cao; Bin Jiang; Xuhui Fan; Xiao-ming Wu; Xianxun Zhu
Venue: AAAI 2025 (main)

Abstract: Graph anomaly detection is crucial for identifying anomalous nodes within graphs and addressing applications like financial fraud detection and social spam detection. Recent spectral graph neural network methods advance graph anomaly detection by focusing on anomalies that notably affect the distribution of graph spectral energy. Such spectrum-based methods rely on two steps: graph wavelet extraction and feature fusion. However, both steps are hand-designed, capturing incomprehensive anomaly information of wavelet-specific features and resulting in their inconsistent feature fusion. To address these problems, we propose a dynamic spectral graph anomaly detection framework DSGAD to adaptively capture comprehensive anomaly information and perform consistent feature fusion. DSGAD introduces dynamic wavelets, consisting of trainable wavelets to adaptively learn anomalous patterns and capture wavelet-specific features with comprehensive anomaly information. Furthermore, the consistent fusion of wavelet-specific features achieves dynamic fusion by combining wavelet-specific feature extraction with energy difference and channel convolution fusion using location correlation. Experimental results on four datasets substantiate the efficacy of our DSGAD method, surpassing state-of-the-art methods in both homogeneous and heterogeneous graphs.
ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting
Authors: Qi Zheng; Zihao Yao; Yaying Zhang
Venue: AAAI 2025 (main)

Abstract: Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods; 2) overlooking spatial correlations across variables over time; 3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability.
Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification
Authors: Zijie Zhou; Zhaoqi Lu; Xuekai Wei; Rongqin Chen; Shenghui Zhang; Pak Lon Ip; Leong Hou U
Venue: AAAI 2025 (main)

Abstract: Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers were introduced to address these issues, achieving a global receptive field but suffering from the noise of irrelevant nodes and loss of structural information. Therefore, drawing inspiration from fine-grained token-based representation learning in Natural Language Processing (NLP), we propose the Structure-aware Multi-token Graph Transformer (Tokenphormer), which generates multiple tokens to effectively capture local and structural information and explore global information at different levels of granularity. Specifically, we first introduce the walk-token generated by mixed walks consisting of four walk types to explore the graph and capture structure and contextual information flexibly. To ensure local and global information coverage, we also introduce the SGPM-token (obtained through the Self-supervised Graph Pre-train Model, SGPM) and the hop-token, extending the length and density limit of the walk-token, respectively. Finally, these expressive tokens are fed into the Transformer model to learn node representations collaboratively. Experimental results demonstrate that the capability of the proposed Tokenphormer can achieve state-of-the-art performance on node classification tasks.
The Adaptive Q-Network for Recommendation Tasks with Dynamic Item Space
Authors: Jianxiang Zhu; Dandan Lai; Zhongcui Ma; Yaxin Peng
Venue: AAAI 2025 (main)

Abstract: Reinforcement learning (RL) algorithms can improve recommendation performance by capturing long-term user-system interaction. However, current RL-based recommendation tasks seldom consider the dynamism of the environment, and standard RL algorithms are ineffective in recommending items dynamically. In addressing these issues, we design a novel task termed dynamic recommendation, which takes the emergence of real-world recommendable items into consideration. Meanwhile, we propose Adaptive Q-Network (AdaQN) to tackle the dynamic recommendation task. Firstly, AdaQN predicts the value of different action characteristics, particularly during the testing phase, which can capture emerging new action characteristics. The above procedure helps AdaQN in effectively adapting to the dynamic action space. Secondly, AdaQN establishes a stable mapping that projects the discrete action space onto a continuous characteristic space. Finally, AdaQN employs a lightweight Q-network design, which mitigates the complexity of the optimization process. Extensive experiments demonstrate that our approach has achieved state-of-the-art performance in the dynamic recommendation task.
Representation Learning Based Predicate Invention on Knowledge Graphs
Authors: Man Zhu; Pengfei Huang; Lei Gu; Xiaolong Xu; Jingyu Han
Venue: AAAI 2025 (main)

Abstract: The recognition of whether or not a predicate should be invented is an important problem in the domain of predicate invention.
Despite its significance, existing research has yet to fully harness the rich data available in knowledge graphs. In this paper, we introduce a novel problem formulation, ReLPI (Representation Learning for Predicate Invention in Knowledge Graphs), marking a pioneering effort in this domain.
To address the core issues of ReLPI, we devise a scoring function that informs the learning process. By optimizing embeddings towards this scoring function, we endow them with semantic meaning, crucial for capturing the nuances of predicate presence patterns.
Furthermore, we present SEmPI (Semantic Embeddings for Predicate Invention), a framework that leverages predicate (relation) embeddings as a trainable medium. SEmPI uncovers latent patterns governing predicate occurrences in knowledge graphs, enabling the invention of novel predicates grounded in these discovered patterns. This approach represents a significant step forward in leveraging data-driven methods for predicate invention in knowledge graphs.
We evaluate the proposed approach on FB15k and DRKG datasets, and the results demonstrate the effectiveness of SEmPI in discovering new predicates.
Addressing Cold-Start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
Authors: Wenqiao Zhu; Lulu Wang; Jun Wu
Venue: AAAI 2025 (main)

Abstract: Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users. The Embedding and MLP paradigm has become a standard approach for industrial recommendation systems and has been widely deployed. However, this paradigm suffers from cold-start problems, where there is either no or only limited user action data available, leading to poorly learned ID embeddings. The cold-start problem hampers the performance of new items. To address this problem, we design a novel diffusion model to generate a warmed-up embedding for new items.
Specifically, we define a novel diffusion process between the ID embedding space and the side information space.
In addition, we can derive a sub-sequence from the diffusion steps to expedite training, given that our diffusion model is non-Markovian.
Our diffusion model is supervised by both the variational inference and binary cross-entropy objectives, enabling it to generate warmed-up embeddings for items in both the cold-start and warm-up phases.
Additionally, we have conducted extensive experiments on three recommendation datasets. The results confirmed the effectiveness of our approach.
Ghidorah: Towards Robust Multi-Scale Information Diffusion Prediction via Test-Time Training
Authors: Wenting Zhu; Chaozhuo Li; Litian Zhang; Senzhang Wang; Xi Zhang
Venue: AAAI 2025 (main)

Abstract: Information diffusion prediction (IDP) is a pivotal task for understanding the dynamics of information propagation within social networks. Conventional models typically adhere to a fixed learning-based paradigm, where the trained prediction model remains static during the inference phase. This paradigm presupposes that the data is independent and identically distributed, an assumption that may not hold true due to the inherently open nature of social media and the uncertainty and variability in user behavior. In this paper, we address the novel problem of out-of-distribution (OOD) shifts within IDP tasks and propose a new test-time training-based model for multi-scale IDP tasks, named Ghidorah. Our approach focuses on adapting a subset of model parameters to accommodate the unique characteristics of test samples through self-supervised learning (SSL) tasks. Ghidorah comprises three components: the macroscopic prediction branch, the microscopic prediction branch, and the auxiliary SSL branch. The auxiliary SSL task employs a masked autoencoder-based loss to fine-tune the model for specific test samples prior to prediction. Furthermore, Ghidorah integrates invariant learning to capture robust representations while mitigating spurious correlations. To our knowledge, Ghidorah is the first work to introduce a test-time training framework specifically designed to address the critical yet often overlooked OOD challenges in IDP. Experimental results across several benchmark datasets validate the superiority of our approach.
Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement
Authors: Shuman Zhuang; Zhihao Wu; Zhaoliang Chen; Hong-Ning Dai; Ximeng Liu
Venue: AAAI 2025 (main)

Abstract: Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data.
However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions.
To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures.
Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline.
However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness.
To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods.
Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information.
Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges.
Additionally, we also provide an improved GNN as classifier.
Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE.
Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks.
Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection
Authors: Zhipeng Zou; Sheng Wan; Guangyu Li; Bo Han; Tongliang Liu; Lin Zhao; Chen Gong
Venue: AAAI 2025 (main)

Abstract: Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the centroids of in-distribution (ID) examples, have demonstrated promising OOD detection performance. However, the objectives utilized in prior approaches are typically designed for classification and thus might not yield sufficient discriminative power to distinguish between ID and OOD examples. Therefore, this paper proposes a prototype-based contrastive learning framework for OOD detection, which is termed provable Discriminative Hyperspherical Embedding (DHE). The proposed framework provides a theoretical analysis of inter-class dispersion, which is proved to be fundamental in reducing the false positive rate (FPR) on OOD examples. Based on this, we devise an angular spread loss to achieve the maximal dispersion of the prototypes of different classes prior to training. Subsequently, a prototype-enhanced contrastive loss is introduced to align embeddings of ID examples closely with their corresponding prototypes. In our proposed DHE, the maximal prototype dispersion is theoretically proved, thereby avoiding the pitfalls of local optima commonly encountered by most existing methods. Experimental results demonstrate the effectiveness of our proposed DHE, which showcases a remarkable reduction in FPR95 (i.e., 5.37% on CIFAR-100) and more than doubling the computational efficiency when compared with the state-of-the-art methods.