Title: Graphs Help Graphs: Multi-Agent Graph Socialized Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Neural Network

Socialized Learning

Graph Collaborative Learning

Graph Lifelong Learning
Abstract: Graphs in the real world are fragmented and dynamic, lacking collaboration akin to that observed in human societies. Existing paradigms present collaborative information collapse and forgetting, making collaborative relationships poorly autonomous and interactive information insufficient. Moreover, collaborative information is prone to loss when the graph grows. Effective collaboration in heterogeneous dynamic graph environments becomes challenging. Inspired by social learning, this paper presents a Graph Socialized Learning (GSL) paradigm. We provide insights into graph socialization in GSL and boost the performance of agents through effective collaboration. It is crucial to determine with whom, what, and when to share and accumulate information for effective GSL. Thus, we propose the ''Graphs Help Graphs'' (GHG) method to solve these issues. Specifically, it uses a graph-driven organizational structure to select interacting agents and manage interaction strength autonomously. We produce customized synthetic graphs as an interactive medium based on the demand of agents, then apply the synthetic graphs to build prototypes in the life cycle to help select optimal parameters. We demonstrate the effectiveness of GHG in heterogeneous dynamic graphs by an extensive empirical study. The code is available through https://github.com/Jillian555/GHG.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors formalize **multi-agent graph learning** (MAGL), where multiple agents, each with its own graph, collaborate to train GNNs.\n\nThen, they show the limitations of existing paradigms for MAGL.\n\n- **Graph Federated Learning** breaks down under graph heterogeneity (it treats all peers equally and forces aggregation even when graphs differ wildly), and\n- **Graph Lifelong Learning** doesn’t leverage true peer-to-peer collaboration and still suffers from catastrophic forgetting.\n\nAs an alternative, they introduce **Graph Socialized Learning**, which lets agents learn **who** to collaborate with (via a learned collaboration graph), **what** to exchange (on-demand synthetic subgraphs tailored to each peer), and **when** to communicate (using a prototype-based lifecycle), thereby eliminating forgetting and handling heterogeneity.\n\nExtensive experiments on five citation/social-network benchmarks show that GSL achieves up to ~86% accuracy **with zero forgetting**, substantially outperforming federated, lifelong, and hybrid baselines.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n- **[S1] Novel Paradigm**: Learning *who*, *what*, and *when* to communicate is a fresh take on MAGL, moving beyond “one-size-fits-all” aggregation.\n- **[S2] Effective Synthetic Messaging**: Tailored subgraphs offer targeted knowledge transfer while preserving privacy and reducing bandwidth.\n- **[S3] Zero-Forgetting Guarantee**: Prototype-driven replay convincingly prevents catastrophic forgetting without storing raw data.\n- **[S4] Empirical Rigor**: Thorough ablations, heterogeneity sweeps, and agent-scale analyses validate each component’s contribution.\n\n# Weaknesses\n\n- **[W1] Scalability of Collaboration Graph: It is unclear whether it is scalable to learn an A×A adjacency for large A, and related discussions are lacking.**\n- **[W2] Prototype effectiveness:** Using a single average embedding per class/task might be too coarse in highly heterogeneous or multimodal settings.\n- **[W3] Dataset Diversity:** All benchmarks are citation or social networks. It’d be good to see experiments on biological graphs, e-commerce interaction data, or truly evolving graphs to test generalization.\n- **[W4] Privacy and security guarantees:** Synthetic subgraphs are safer than raw data, but no formal privacy proofs or adversarial-robustness analyses are provided.\n\nQUESTIONS:\n- **[Q1]** The zero-forgetting claim relies on storing and replaying task prototypes and synthetic subgraphs. Can you clarify the total memory footprint of these stored items for GSL and confirm that all baselines were given an equivalent memory budget when measuring forgetting? If not, how might unequal memory allowances bias the comparison?\n- **[Q2]** How does the complexity (compute and storage) of learning an A by A collaboration matrix scale as A grows, and have you considered sparse or hierarchical approximations for large-scale deployments?\n- **[Q3]** Have you evaluated whether a single average embedding per class/task sufficiently captures complex or multimodal distributions? Would using multiple prototypes per class improve performance?\n- **[Q4]** Given that all experiments use citation/social graphs, how do you expect GSL to perform on, say, biological networks, user-item graphs, or truly dynamic graphs with evolving topology?\n- **[Q5]** Beyond qualitative assertions, can you provide formal privacy bounds or adversarial-robustness analyses for your synthetic-graph exchanges to rule out potential information leakage?\n\nLIMITATIONS:\nYes. In Appendix E.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a Graph Socialized Learning (GSL) paradigm to address collaboration challenges in fragmented and dynamic graphs. The proposed method, Graphs Help Graphs (GHG), determines with whom, what, and when to share and accumulate information for effective multi-agent learning. GHG utilizes a graph-driven organizational structure to autonomously select interacting agents and manage interaction strength, generates customized synthetic graphs based on agent demands, and applies them to build prototypes over the life cycle. Extensive empirical studies validate the effectiveness of GHG in heterogeneous dynamic graph environments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed GHG (Graphs Help Graphs) is both well-motivated and novel. It addresses real challenges in multi-agent graph learning with a fresh perspective.\n2. The GHG method combines graph structure, synthetic data generation, and task-aware collaboration in a unified and creative way. The method is structured around three intuitive questions—who to collaborate with, what to share, and when to share—making the approach easy to understand and practically meaningful.\n3. The approach shows consistently strong performance across several benchmarks, outperforming existing baselines in both accuracy and forgetting.\n4. The paper is well-written and clearly organized. The technical content is presented logically, and the mathematical derivations are sound and easy to follow.\n\nWeaknesses:\n1. Although the paper claims the method is efficient, it lacks detailed analysis and comparison of actual time, memory, and communication costs, which are important for real-world deployment. Simply reporting MAP across different interaction rounds is not sufficient to support the efficiency claim.\n2. The paper assumes agents can accurately identify task IDs using prototypes, but it doesn’t deeply discuss what happens if this step fails or is noisy.\n3. All experiments are on benchmark datasets. A real-world example (e.g., in citation networks or recommendation systems) would help show practical value.\n\nQUESTIONS:\n1. Can the authors provide a more comprehensive analysis of the computational and communication costs of GHG?\n2. How robust is the task prototype mechanism to errors in task ID prediction, especially in more complex or overlapping tasks? And how does GHG perform under partial or unreliable collaboration—e.g., limited agent availability or communication failures?\n\nLIMITATIONS:\nWhile the authors acknowledge that \"the primary limitation of GHG lies in its insufficiency to consider relationships among graph tasks for multiple agents,\" I believe the paper would benefit from a broader discussion of the limitations of GSL and GHG compared to existing paradigms such as Graph Federated Learning (GFL) and Graph Lifelong Learning (GLL).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nEffective collaboration in heterogeneous dynamic graph environments becomes challenging. Inspired by social learning, the paper presents  Graph Socialized Learning (GSL) paradigm. The paper has following main contributions:\n• Authors present a practical learning paradigm called Graph Socialized Learning (GSL), enabling each agent’s growth via collaborative interaction.\n• Authors describe how Graph-driven organizational structure, customized interaction medium, and prototype-based life cycle form three key elements of socialized collaboration.\n• Proposed method is claimed to consistently achieves performance improvements on multiple datasets and demonstrates the effectiveness of all components.\n\nTo determine with whom, what, and when to share and accumulate information for effective GSL “Graphs Help Graphs” method is proposed (GHG). GHG uses graph-driven organizational structure to autonomously select interacting agents and manage interaction strength. In addition, synthetic graphs are built as interaction medium based on demand of agents. These help to build prototypes in life cycle to help select optimal parameters. An empirical study accompanies that demonstrates the effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nAuthors should work on answering following to improve the quality of submission:\n1. what is good example of the scenario mentioned regarding complete new info being learned each time\n2. the data sets used for benchmarking have been out in the open and may be learned, how do we check for that\n3. isnt artificial partitioning using spectral partitioning giving away info on the labels\n4. It is unclear how many agents are sufficient and what is the interrelationship between the number of agents and algo perf.\n5. lot of hand waving in the paper than describing actual algo level details of how the social part happens and what is the inter-relationship of the Experimental tasks on the chosen five datasets vis-a-vis this social interaction.\n\nQUESTIONS:\n1. what is good example of the scenario mentioned regarding complete new info being learned each time\n2. the data sets used for benchmarking have been out in the open and may be learned, how do we check for that\n3. isnt artificial partitioning using spectral partitioning giving away info on the labels\n4. It is unclear how many agents are sufficient and what is the interrelationship between the number of agents and algo perf.\n5. lot of hand waving in the paper than describing actual algo level details of how the social part happens\n\nLIMITATIONS:\n1. what is good example of the scenario mentioned regarding complete new info being learned each time\n2. the data sets used for benchmarking have been out in the open and may be learned, how do we check for that\n3. isnt artificial partitioning using spectral partitioning giving away info on the labels\n4. It is unclear how many agents are sufficient and what is the interrelationship between the number of agents and algo perf.\n5. lot of hand waving in the paper than describing actual algo level details of how the social part happens", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors address challenges of information collapse and catastrophic forgetting in distributed graph learning by introducing the Graph Socialized Learning paradigm. To realize this, the authors propose the Socialized Collaboration Paradigm, which formalizes “who to collaborate with, what information to share, and when to recall past knowledge” in a unified Bayesian framework for continual, heterogeneously distributed graph tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n1. The paper is well‑structured and logically sound, guiding readers smoothly from motivation through methodology to theoretical and empirical findings.\n\n2. Experiment 5.4 convincingly demonstrates the necessity of collaboration and confirms the algorithm’s ability to enable efficient information exchange.\n\n**Weakness**\n\n1.  The proposed method is relatively complex, incorporating multiple technical components such as subspace complementarity, model/structure similarity, distribution‑aligned synthetic graphs, and prototype‑based retrieval. While this design is principled, the number of interacting modules and hyperparameters may make the approach harder to tune and reproduce in practice.\n\n2.  The prototype pool may gradually grow as new tasks and classes are added, potentially introducing storage overhead. While pruning can mitigate this, it relies on carefully chosen strategies and thresholds.\n\nQUESTIONS:\n1. The paper states that “D-GFL fails to capture agent dependencies,” but recent variants (e.g., PushSum-GNN) incorporate structure-aware designs. Could the authors clarify whether this refers only to standard D-GFL methods?\n2. The current ablation study focuses on removing individual components in isolation, which limits insight into how different modules interact. Would it be beneficial to explore additional combinations—such as testing pairs of modules—to better understand their joint contributions and potential redundancy?\n\n3. How sensitive is GHG to the choice of hyperparameters, such as the collaboration threshold or prototype pool size? Has this been empirically evaluated?\n\nLIMITATIONS:\nsee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}