Title: GLNCD: Graph-Level Novel Category Discovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Novel category discovery

Open-world learning

Graph neural network
Abstract: Graph classification has long assumed a closed-world setting, limiting its applicability to real-world scenarios where new categories often emerge. To address this limitation, we introduce Graph-Level Novel Category Discovery (GLNCD), a new task aimed at identifying unseen graph categories without supervision from novel classes. We first adapt classical Novel Category Discovery (NCD) methods for images to the graph domain and evaluate these baseline methods on four diverse graph datasets curated for the GLNCD task. Our analysis reveals that these methods suffer a notable performance degradation compared to their image-based counterparts, due to two key challenges: (1) insufficient utilization of structural information in graph self-supervised learning (SSL), and (2) ineffective pseudo-labeling strategies based on ranking statistics (RS) that neglect graph structure. To alleviate these issues, we propose ProtoFGW-NCD, a framework consisting of two core components: ProtoFGW-CL, a novel graph SSL framework, and FGW-RS, a structure-aware pseudo-labeling method. Both components employ a differentiable Fused Gromov-Wasserstein (FGW) distance to effectively compare graphs by incorporating structural information. These components are built upon learnable prototype graphs, which enable efficient, parallel FGW-based graph comparisons and capture representative patterns within graph datasets. Experiments on four GLNCD benchmark datasets demonstrate the effectiveness of ProtoFGW-NCD.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors explored the task of discovering new classes in the graph-level tasks. They begin by analyzing the performance of existing CV methods on graph data, highlighting their inability to effectively utilize structural information. To address this, the authors introduced the Gromov-Wasserstein distance and designed a method that can simultaneously encode both structural and features information by calculating the distance to many learnable prototype graphs. The authors claimed that experiments showed the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The authors explore a novel and valuable problem, which is discovering new classes in graph-level tasks.\n\n- The proposed method is innovative. The authors design multiple prototype graphs and calculate the distribution distance between graphs and prototype graphs to obtain node representations. This idea provides valuable insights to the readers.\n\n- The authors' analysis of CV methods is valuable.\n\nWeaknesses:\n\n- The primary drawback of this paper, in my view, is that the proposed method does not seem to work. In Tables 3 and 4, the improvements of the proposed method compared to existing CV methods are too marginal. Particularly in Table 4, the results for new classes show that the proposed method achieves the same rank of 2 as AutoNovel, indicating no improvement.\n\n- The organization of Section 5 could be improved to enhance clarity. The authors might consider providing an overview of the entire method first, then detailing each part individually. Additionally, I suggest including more explanations to clarify the rationale behind each step, which might make it easier for readers to follow. While I understand the challenge of explaining thoroughly within limited space, the authors could make more references to the appendix and provide additional details there.\n\n- The motivation for selecting the Gromov-Wasserstein distance is missing. There are many existing methods to measure structural similarity. The authors should explain why Gromov-Wasserstein was chosen over others.\n\n- The experiments lack a report on the results of the balance weight $\\alpha$ between structure and feature. It is a learnable parameter, and I believe the authors should provide the learned values to illustrate the extent to which the proposed method leverages structural information.\n\n- I think the authors need to report the results of the proposed method under the settings of Figures 1 and 2 for a comprehensive evaluation of the proposed method.\n\nQUESTIONS:\nPlease see the Weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-15"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study investigates the traditional Novel Class Discovery problem in the context of graph-level classification. The authors first introduce several graph datasets suitable for NCD evaluation and design three Graph-Level NCD baselines based on existing image-based NCD methods. Subsequently, they analyze these baselines and identify key limitations that conventional visual NCD designs fail to effectively incorporate graph structural information, leading to suboptimal performance on graph data. To address this issue, the authors propose a novel approach based on optimal transport that integrates graph structural information.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This work appears to be the first to explore NCD in the context of graph-level classification. Potential applications, such as novel drug molecule discovery, could benefit from this research.  \n2. The experimental evaluation and analysis of the baselines reveal unique challenges specific to NCD on graph-structured data.  \n3. The authors design and implement an efficient and differentiable Fused Gromov-Wasserstein distance module, which facilitates the application of FGW in graph-level tasks.  \n\nWeaknesses\n1. The study employs a relatively limited number of datasets and baselines.  \n2. The discussion on the challenges of Representation Similarity in graph data (Section 4.2) is somewhat unclear. A more detailed, point-by-point analysis with additional elaboration would strengthen this section.  \n3. The meaning of the \"Avg. Rank/All\" column in Tables 3 and 4 is not explicitly explained. Does it represent the average ranking across all preceding columns?  \n4. The proposed BAPG layer bears similarity to the method presented in [1], but a detailed comparison between the two is lacking.  \n\n[1] Template Based Graph Neural Network with Optimal Transport Distances, NeurIPS 2022.\n\nQUESTIONS:\nSee in Weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Graph-Level Novel Category Discovery (GLNCD), extending graph classification to open-world scenarios where models must discover novel graph categories without supervision. The authors propose ProtoFGW-NCD, addressing limitations of adapted visual NCD methods through two components: ProtoFGW-CL and FGW-RS. A parallel BAPG layer enables efficient FGW computation. Experiments on four diverse datasets show consistent improvements over adapted visual NCD baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n1. The transition from closed-world to open-world graph classification addresses a genuine need in applications. While node-level NCD has been explored in prior work [1], this appears to be the first systematic study of graph-level NCD, representing a meaningful contribution to the graph learning literature.\n\n\n2. The controlled experiments in Figures 1-2 effectively demonstrate that traditional ranking statistics fail on graph data even with improved representations. The insight that structural information loss, rather than representation quality, is the bottleneck provides clear motivation for the proposed FGW-based approach.\n\n\n3. The authors develop a differentiable BAPG layer that enables efficient batch computation of FGW distances. The learnable prototype mechanism elegantly solves the variable-sized graph comparison problem.\n\n**Weaknesses**:\n1. While FGW distance is reasonable for graph comparison, the paper lacks a systematic comparison with other graph distance measures (e.g., Graph Edit Distance) or alternative approaches to structure-aware SSL. The choice appears more empirically driven than theoretically necessary, and the complexity-benefit trade-off is not thoroughly analyzed.\n\n2. Given that FGW distance already provides a graph similarity measure, the insistence on using ranking statistics seems like conservative adherence to traditional NCD pipelines rather than optimal design. Why not directly use FGW distances for similarity judgment? The paper doesn't convincingly justify why ranking is still beneficial when operating on structure-aware FGW vectors.\n\n3. The simple node/edge dropping augmentation seems naive for graph data. Have the authors considered more sophisticated graph augmentations that preserve semantic structure? The choice of augmentation strategy could significantly impact the quality of self-supervised learning.\n\n[1] Jin, Y., Xiong, Y., Fang, J., Wu, X., He, D., Jia, X., ... & Yu, P. S. (2024, July). Beyond the Known: Novel Class Discovery for Open-World Graph Learning. In International Conference on Database Systems for Advanced Applications (pp. 117-133). Singapore: Springer Nature Singapore.\n\n[2] Hou, Y., Chen, X., Zhu, H., Liu, R., Shi, B., Liu, J., ... & Xu, K. (2024, October). NC^2D: Novel Class Discovery for Node Classification. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 849-859).\n\nQUESTIONS:\n1. How sensitive is performance to the number of prototype graphs K? \n\n2. How does the α parameter in the FGW distance affect performance across different graph types and domains?\n\nLIMITATIONS:\nSee Weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}