4 papers
HeRB: Heterophily-Resolved Structure Balancer for Graph Neural Networks
Ke-Jia Chen, Wenhui Mu, Zheng Liu
Recent research has witnessed the remarkable progress of Graph Neural Networks (GNNs) in the realm of graph data representation. However, GNNs still encounter the challenge of stru…
HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Shilong Sang, Ke-Jia Chen, Zheng liu
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However…
Balancing Augmentation with Edge-Utility Filter for Signed GNNs
Ke-Jia Chen, Yaming Ji, Youran Qu +1
Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The exi…
Layer Imbalance Aware Multiplex Network Embedding
Kejia Chen, Yinchu Qiu, Zheng Liu
Multiplex network embedding is an effective technique to jointly learn the low-dimensional representations of nodes across network layers. However, the number of edges among layers…