Addressing Heterogeneity and Heterophily in Graphs: A Heterogeneous Heterophilic Spectral Graph Neural Network

no code implementations • 17 Oct 2024 • Kangkang Lu, Yanhua Yu, Zhiyong Huang, Jia Li, Yuling Wang, Meiyu Liang, Xiting Qin, Yimeng Ren, Tat-Seng Chua, Xidian Wang

Specifically, we propose a Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which employs a dual-module approach: local independent filtering and global hybrid filtering.

Graph Neural Network

Federated Learning over Coupled Graphs

no code implementations • 26 Jan 2023 • Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, Xiaohong Guan

In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.

Federated Learning Node Classification