Advanced Graph Neural Networks

Derniers papiers

• Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization

  Zhicheng Wang, Chen Ju, Xu Chen, Shuai Xiao, Jinsong Lan, Xiaoyong Zhu, Ying Chen, Zhiguo Cao · 4 novembre 2025

  Embedding models are a cornerstone of modern AI. Driven by Multimodal Large Language Models (MLLMs), they have made great progress in architecture and data curation, while the holistic paradigm is still limited to SSC, i.e., single input, singular embedding, contrastive supervision, which collapses …

• Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links

  Jiahua Lu, Huaxiao Liu, Shuotong Bai, Junjie Xu, Renqiang Luo, Enyan Dai · 4 novembre 2025

  Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications. However, due to the biases in the graph structures, graph neural networks face significant challenges in fairness. Although the original user graph structure is generally biased, it is promising to guide these…

• Efficient Curvature-aware Graph Network

  Chaoqun Fei, Tinglve Zhou, Tianyong Hao, Yangyang Li · 4 novembre 2025

  Graph curvature provides geometric priors for Graph Neural Networks (GNNs), enhancing their ability to model complex graph structures, particularly in terms of structural awareness, robustness, and theoretical interpretability. Among existing methods, Ollivier-Ricci curvature has been extensively st…

• Over-squashing in Spatiotemporal Graph Neural Networks

  Ivan Marisca, Jacob Bamberger, Cesare Alippi, Michael M. Bronstein · 4 novembre 2025

  Graph Neural Networks (GNNs) have achieved remarkable success across various domains. However, recent theoretical advances have identified fundamental limitations in their information propagation capabilities, such as over-squashing, where distant nodes fail to effectively exchange information. Whil…

• MeixnerNet: Adaptive and Robust Spectral Graph Neural Networks with Discrete Orthogonal Polynomials

  Huseyin Goksu · 4 novembre 2025

  Spectral Graph Neural Networks (GNNs) have achieved state-of-the-art results by defining graph convolutions in the spectral domain. A common approach, popularized by ChebyNet, is to use polynomial filters based on continuous orthogonal polynomials (e.g., Chebyshev). This creates a theoretical discon…

• Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation

  Xin Hao, Changyang She, Phee Lep Yeoh, Yuhong Liu, Branka Vucetic, Yonghui Li · 4 novembre 2025

  In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available res…

• BadGraph: A Backdoor Attack Against Latent Diffusion Model for Text-Guided Graph Generation

  Liang Ye, Shengqin Chen, Jiazhu Dai · 4 novembre 2025

  The rapid progress of graph generation has raised new security concerns, particularly regarding backdoor vulnerabilities. While prior work has explored backdoor attacks in image diffusion and unconditional graph generation, conditional, especially text-guided graph generation remains largely unexami…

• GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation

  Zihao Guo, Qingyun Sun, Ziwei Zhang, Haonan Yuan, Huiping Zhuang, Xingcheng Fu, Jianxin Li · 4 novembre 2025

  Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL approaches focus on task-incremental and class-incremental scenarios within a single domain. Graph domain-incremental learn…

• DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform

  Peizhi Niu, Yu-Hsiang Wang, Vishal Rana, Chetan Rupakheti, Abhishek Pandey, Olgica Milenkovic · 4 novembre 2025

  We introduce a new graph diffusion model for small molecule generation, DMol, which outperforms the state-of-the-art DiGress model in terms of validity by roughly 1.5% across all benchmarking datasets while reducing the number of diffusion steps by at least 10-fold, and the running time to roughly o…

• Fixed-point graph convolutional networks against adversarial attacks

  Shakib Khan, A. Ben Hamza, Amr Youssef · 4 novembre 2025

  Adversarial attacks present a significant risk to the integrity and performance of graph neural networks, particularly in tasks where graph structure and node features are vulnerable to manipulation. In this paper, we present a novel model, called fixed-point iterative graph convolutional network (F…

• Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought

  Hanlin Zhu, Shibo Hao, Zhiting Hu, Jiantao Jiao, Stuart Russell, Yuandong Tian · 4 novembre 2025

  Large Language Models (LLMs) have demonstrated remarkable performance in many applications, including challenging reasoning problems via chain-of-thoughts (CoTs) techniques that generate ``thinking tokens'' before answering the questions. While existing theoretical works demonstrate that CoTs with d…

• DynBERG: Dynamic BERT-based Graph neural network for financial fraud detection

  Omkar Kulkarni, Rohitash Chandra · 4 novembre 2025

  Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BER…

• Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Serving

  Chengying Huan, Ziheng Meng, Yongchao Liu, Zhengyi Yang, Yun Zhu, Yue Yun, Shipeng Li, Rong Gu, Xiabao Wu, Haitao Zhang, Chuntao Hong, Shaonan Ma, Guihai Chen, Chen Tian · 4 novembre 2025

  Graph Chain-of-Thought (Graph-CoT) enables large language models (LLMs) to perform step-by-step reasoning over graph-structured knowledge, but existing pipelines suffer from low accuracy, excessive token usage, high latency, and low throughput due to single-agent monolithic prompts, repeated context…

• DARTS-GT: Differentiable Architecture Search for Graph Transformers with Quantifiable Instance-Specific Interpretability Analysis

  Shruti Sarika Chakraborty, Peter Minary · 3 novembre 2025

  Graph Transformers (GTs) have emerged as powerful architectures for graph-structured data, yet remain constrained by rigid designs and lack quantifiable interpretability. Current state-of-the-art GTs commit to fixed GNN types across all layers, missing potential benefits of depth-specific component …

• RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphs

  Joe Meyer, Divyansha Lachi, Mahmoud Mohammadi, Roshan Reddy Upendra, Eva L. Dyer, Mark Li, Tom Palczewski · 3 novembre 2025

  Relational multi-table data is common in domains such as e-commerce, healthcare, and scientific research, and can be naturally represented as heterogeneous temporal graphs with multi-modal node attributes. Existing graph neural networks (GNNs) rely on schema-specific feature encoders, requiring sepa…

• FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance

  Minh Phu Vuong, Young-Ju Lee, Iv\'an Ojeda-Ruiz, Chul-Ho Lee · 3 novembre 2025

  Due to the growing concern about unsavory behaviors of machine learning models toward certain demographic groups, the notion of 'fairness' has recently drawn much attention from the community, thereby motivating the study of fairness in graph clustering. Fair graph clustering aims to partition the s…

• Spectral Neural Graph Sparsification

  Angelica Liguori, Ettore Ritacco, Pietro Sabatino, Annalisa Socievole · 3 novembre 2025

  Graphs are central to modeling complex systems in domains such as social networks, molecular chemistry, and neuroscience. While Graph Neural Networks, particularly Graph Convolutional Networks, have become standard tools for graph learning, they remain constrained by reliance on fixed structures and…

• Geometry-Aware Edge Pooling for Graph Neural Networks

  Katharina Limbeck, Lydia Mezrag, Guy Wolf, Bastian Rieck · 3 novembre 2025

  Graph Neural Networks (GNNs) have shown significant success for graph-based tasks. Motivated by the prevalence of large datasets in real-world applications, pooling layers are crucial components of GNNs. By reducing the size of input graphs, pooling enables faster training and potentially better gen…

• Topology-Aware Active Learning on Graphs

  Harris Hardiman-Mostow, Jack Mauro, Adrien Weihs, Andrea L. Bertozzi · 31 octobre 2025

  We propose a graph-topological approach to active learning that directly targets the core challenge of exploration versus exploitation under scarce label budgets. To guide exploration, we introduce a coreset construction algorithm based on Balanced Forman Curvature (BFC), which selects representativ…

• Robust Graph Condensation via Classification Complexity Mitigation

  Jiayi Luo, Qingyun Sun, Beining Yang, Haonan Yuan, Xingcheng Fu, Yanbiao Ma, Jianxin Li, Philip S. Yu · 31 octobre 2025

  Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates s…

• Flex-GAD : Flexible Graph Anomaly Detection

  Apu Chakraborty, Anshul Kumar, Gagan Raj Gupta · 31 octobre 2025

  Detecting anomalous nodes in attributed networks, where each node is associated with both structural connections and descriptive attributes, is essential for identifying fraud, misinformation, and suspicious behavior in domains such as social networks, academic citation graphs, and e-commerce platfo…

• LINK-KG: LLM-Driven Coreference-Resolved Knowledge Graphs for Human Smuggling Networks

  Dipak Meher, Carlotta Domeniconi, Guadalupe Correa-Cabrera · 31 octobre 2025

  Human smuggling networks are complex and constantly evolving, making them difficult to analyze comprehensively. Legal case documents offer rich factual and procedural insights into these networks but are often long, unstructured, and filled with ambiguous or shifting references, posing significant c…

• Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs

  Dipak Meher, Carlotta Domeniconi · 31 octobre 2025

  Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer critical insights but are often unstructured, lexically dense, and filled with ambiguous or shifting references, which pose significant challenges for automated knowledge graph (KG) construction. …

• HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling

  Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli · 31 octobre 2025

  Graphs model latent variable relationships in many real-world systems, and Message Passing Neural Networks (MPNNs) are widely used to learn such structures for downstream tasks. While edge-based MPNNs effectively capture local interactions, their expressive power is theoretically bounded, limiting t…

• Understanding Generalization in Node and Link Prediction

  Antonis Vasileiou, Timo Stoll, Christopher Morris · 31 octobre 2025

  Using message-passing graph neural networks (MPNNs) for node and link prediction is crucial in various scientific and industrial domains, which has led to the development of diverse MPNN architectures. Besides working well in practical settings, their ability to generalize beyond the training set re…