Results 1 to 10 of about 39 (39)

Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree

Applied Sciences, 2022
Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with ...
Rui Zhang, Xin Li
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Evaluating explainability for graph neural networks

Scientific Data, 2023
As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial.
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik   +3 more
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CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

BMC Bioinformatics, 2023
Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate ...
Jianqiu Wu, Hongyang Chen, Minhao Cheng, Haoyi Xiong   +3 more
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Reinforcement learning-driven adaptive rewiring modulates fragmentation depth in bounded-confidence opinion dynamics

Frontiers in Physics
The structure of an interaction network strongly shapes opinion clustering and the emergence of echo chambers in bounded-confidence (BC) models. We ask whether a controller can steer this clustering by rewiring edges adaptively and how a learned policy ...
Quang Nguyen, Quang Nguyen, Nha Binh Truong   +2 more
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Graph Anomaly Detection Algorithm Based on Multi-View Heterogeneity Resistant Network

Information
Graph anomaly detection (GAD) aims to identify nodes or edges that deviate from normal patterns. However, the presence of heterophilic edges in graphs leads to feature over-smoothing issues. To overcome this limitation, this paper proposes the multi-view
Yangrui Fan, Caixia Cui, Zhiqiang Wang, Hui Qi, Zhen Tian   +4 more
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Spectral Filtering Using Periodic Autoregressive Moving Average Graph Neural Networks for Heterophilic Graphs

IEEE Access
The importance of Graph Neural Networks (GNNs) has increased over the years due to their ability to handle non-Euclidean data. Most of the existing research mainly focuses on spatial relationships using neighboring nodes to aggregate information, which ...
Baimyrza Kalmyrzayev, Hashim Ali, Muhammad Tahir Akhtar   +2 more
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Preserving Global Information for Graph Clustering with Masked Autoencoders

Mathematics
Graph clustering aims to divide nodes into different clusters without labels and has attracted great attention due to the success of graph neural networks (GNNs).
Rui Chen
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A Mutual Information-Based Framework for Enhancing Graph Neural Networks on Heterophily

IEEE Access
Graph Neural Networks (GNNs) have demonstrated strong capabilities in analyzing structured data, particularly under the assumption of homophily, where neighboring nodes tend to share similar attributes.
Gahee Kim, Seongjin Choi, Se-Young Yun
doaj   +1 more source

AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network

Journal of King Saud University: Computer and Information Sciences
Graph-structured data has been widely used in modern information management systems. Effectively extracting the latent structural and semantic relationships between nodes in the graph is a key research challenge.
Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji   +5 more
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SS-AdaMoE: Spatio-Spectral Adaptive Mixture of Experts with Global Structural Priors for Graph Node Classification

Entropy
Graph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low ...
Xilin Kang, Tianyue Yu, Letao Wang, Yutong Guo, Fengjun Zhang   +4 more
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