Advanced Graph Neural Networks

Neueste Paper

• HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs

  Megha P, Harshit Kumar, Srajan Agarwal, Anirban Banerjee, Olaf Wolkenhauer, Saptarshi Bej · 5. Oktober 2026

  Self-supervised hypergraph representation learning can produce informative node embeddings, but existing methods often require deep encoders trained for hundreds of epochs, making embedding generation costly even for hypergraphs with a few thousand nodes. This limits applications requiring embedding…

• Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs

  Md Abrar Jahin, Taufikur Rahman Fuad, Md Rizwan Parvez · 5. Oktober 2026

  Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive methods encode temporal dependencies implicitly in encoder weights, without an explicit model of how node representations evolve, making them brittle und…

• Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach

  Xunkai Li, Chenxi Wan, Yinlin Zhu, Wang Luo, Hongchao Qin, Rong-Hua Li, Guoren Wang · 5. Oktober 2026

  Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraini…

• Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

  Yue Hou, Ruomei Liu, Yingke Su, Junran Wu, Ke Xu · 5. Oktober 2026

  Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time. Prevailing solutions rely on training auxiliary generative modules to synthesize memory graphs for cross-domain adaptat…

• No-Free-Graph: Learning When Multimodal Data Should Be Graphified

  Zekai Chen, Kai Hu, YuXin Zeng, Xunkai Li, Xun Wu, Yinlin Zhu, Zhengyu Wu, Xu Wang, Rong-Hua Li · 5. Oktober 2026

  Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. Existing studies have made substantial progress on how to construct and optimize graphs, but rarely consider a more fundamental question: whether add…

• Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

  Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu · 5. Oktober 2026

  Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and observational constraints, severely limiting the effectiveness of modern graph learning pipelines. While existing Graph Data Augmentation (GDA) met…

• KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning

  Yisen Gao, Jiaxin Bai, Haoyu Huang, Zhongwei Xie, Yufei Li, Hong Ting Tsang, Sirui Han, Yangqiu Song · 5. Oktober 2026

  Knowledge graph (KG) foundation models aim to generalize to graphs with unseen entities and relations by learning transferable relational structure. Most existing methods, however, focus on relation-level universality, leaving in-context learning, the other pillar of foundation models, largely unexp…

• Rethinking Epistemic Uncertainty in Node Classification through Information Growth

  Emma Meneghini, Francesco Ferrini, Bruno Lepri, Andrea Passerini, Veronica Lachi · 5. Oktober 2026

  Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, existing graph evidential deep learning (EDL) methods for node classification typically construct epistemic uncertainty from graph-specific properties and …

• Hop-Decayed Influence: New Vulnerabilities of Structural Auxiliary Indexing in GraphRAG Pipelines with LLM

  Jisung Park, John Le, Heath Cooper · 5. Oktober 2026

  GraphRAG pipelines construct auxiliary structures during offline indexing--semantic summaries, hierarchical edges, and pre-computed scores--that determine how retrieval is prioritised at query time. Prior attacks target only instance-level components (nodes, edges, triples), overlooking these schema…

• When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders

  Xiaozuo Shen, Yifei Cai, Tian Tan, Rui Ning, Chunsheng Xin, Hongyi Wu · 1. Oktober 2026

  Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees or forests, while post-hoc graphs permit multiple parents but neither guide feature learning nor ensure reliable relation recovery. We introduce the A…

• Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

  Alessio Borgi, Mario Severino, Fabrizio Silvestri, Pietro Li\`o · 1. Oktober 2026

  Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be transformed as it moves across a graph. We introduce \textsc{ESNN}, an Equivariant Sheaf Neural Network that enriches this in…

• GraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics

  Weiqi Jiang, Yuchen Ying, Rui Wang, Kaixuan Chen, Bingde Hu, Shunyu Liu, Yu Wang, Tongya Zheng · 1. Oktober 2026

  Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. However, training capable graph agents typically requires large collections of question-answer pairs and reasoning trajectories, wh…

• HM-ROUTER: Joint Model and Harness Routing for Agentic Systems

  Hao Mark Chen, Royson Lee, Yasuyuki Okoshi, Dimitris Anastasiou, Wayne Luk, Hongxiang Fan · 30. September 2026

  Agent performance depends on both the underlying model and the harness that manages its tool use and execution. Selecting a suitable pair requires accounting for their compatibility, yet training samples may cover only a subset of the growing combination space. We introduce HM-Router, a routing meth…

• Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

  Ying Song, Xiaowei Jia, Balaji Palanisamy · 30. September 2026

  As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical security threat. By querying a victim model's black-box API, an adversary can construct a functionally equivalent surrogate model, compromising proprie…

• GARDiff: Graph-Aligned Residual Diffusion for Probabilistic Multivariate Time-Series Forecasting

  Rui Han, Min Yang, Xu Zhang, Xinghao Yang, Wei Liu, Yongshun Gong · 30. September 2026

  Diffusion models have recently shown strong potential for probabilistic multivariate time-series forecasting by modeling complex conditional distributions. Recent decoupled diffusion frameworks further separate forecasting into deterministic prediction and stochastic residual generation, making it n…

• Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis

  Shehroz Ahmad Shoaib, Kangming Li · 30. September 2026

  Final predictive accuracy is the standard basis for comparing graph neural networks (GNNs) in materials-property prediction, but it does not show how strongly performance depends on access to trainable parameter-space directions. Here, we introduce trainable-degree dependence as a complementary char…

• ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction

  Alberto Bernardi, Luca Costabello, Christophe Gueret · 30. September 2026

  Knowledge Graph Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missing links. However, the quality of the learned representations varies a lot across different areas of the graph. If previous research has loosely link…

• WorldGraph: Graph-Native World Modeling

  Zezhong Ding, Yipeng Li, Xike Xie · 29. September 2026

  World models infer latent states of an environment to capture its underlying dynamics and predict future evolution. Many real-world environments, however, are inherently relational and observed as evolving graphs, where entities, relations, and their properties change over time. Prior graph-related …

• HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases

  Quan D. Bui, Nguyen Do, An Nguyen Dang, Huyen Nguyen, Nhu Duc Minh Nguyen, My T. Thai · 29. September 2026

  Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structural modeling, previous approaches either face quadratic scaling costs or sacrifice explicit source-to-target contribution decomposition. In terms of fea…

• Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily

  Priyanath Maji, Sidharth Gaur, Rajavinoth Paul Durai · 29. September 2026

  Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evaluations typically compare architectures under a fixed node-feature representation, leaving unclear whether conclusions about heterop…

• Investigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspective

  Nikolaos Zafeiropoulos, Emmanouil Mavrikos, George E. Tsekouras · 29. September 2026

  In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The graphs that are used as inputs to the network are constructed by a k-nearest neighbor-based preprocessing procedure, while fairness issues are consid…

• AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks

  Sirui Li, Pietro Li\`o b, Xinsheng Li, Baisong Liu, Chengbin Peng · 29. September 2026

  Hypergraph neural networks have achieved significant success in recent years. However, manual architecture crafting is labor-intensive and often fails to capture complex, higher-order relations, making the automation of hypergraph neural network structure design crucial. To improve the automation an…

• ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models

  Zhongjian Zhang, Xiao Wang, Busheng Zhang, Bo Yan, Xingtong Yu, Yue Gao, Jia Li, Chuan Shi · 29. September 2026

  Zero-shot graph models (ZGMs), which learn transferable knowledge from source graphs and directly apply to unseen target graphs without any adaptation, have achieved promising performance and attracted considerable attention. Despite their proliferation, existing ZGMs are predominantly evaluated on …

• TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks

  Chun Hei Michael Chan, Flavia Petruso, Dimitri Van De Ville · 29. September 2026

  Graph convolutional networks propagate information by repeated local aggregation through a graph shift operator; i.e., a $K$-layer network reaches $K$ hops neighborhood. On the one hand, such spreading can lead to oversmoothing. On the other hand, long-range dependencies demand the depth. Transporti…

• Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs

  Tyler Bonnet, Marek Rei · 29. September 2026

  Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persist across domains. However, existing methods tend to overfit to domain-specific structural, temporal, and semantic patterns, limiting edge classifica…