arXiv Machine Learning
Jul 31
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
arXiv:2209. 00546v5 Announce Type: replace-cross Abstract: Signed and directed networks are ubiquitous in real-world applications.
Nonlinear Laplacians Improve Signed-Directed Graph Learning
arXiv:2608. 00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks.
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arXiv Machine Learning
Jun 16
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
arXiv:2602. 10031v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing.
arXiv Machine Learning
Jun 19
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
arXiv:2510. 16311v3 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information.
arXiv Machine Learning
Jun 10
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
arXiv:2606. 10284v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm.
arXiv AI
Jul 14
Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization
arXiv:2607. 11577v1 Announce Type: cross Abstract: We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.
arXiv Machine Learning
Jun 30
GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.
arXiv AI
Jul 1
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
arXiv:2511. 11046v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data.
arXiv Machine Learning
Jul 7
Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
arXiv Machine Learning
Jul 14
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.
arXiv AI
Jun 8
Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
arXiv:2606. 07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis.
arXiv Machine Learning
Jul 30
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.
arXiv Machine Learning
Jun 25
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
arXiv:2602. 14239v3 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science.