Polarity-based graph neural network for sign prediction in signed bipartite graphs

Feb 16, 2022·
Xianhang Zhang
,
Hanchen Wang
Jianke Yu
Jianke Yu
,
Chen Chen
,
Xiaoyang Wang
,
Wenjie Zhang
· 1 min read
CCF-B CAS Zone 4 JCR Q2
Abstract
Abstract As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.
Type
Publication
World Wide Web
Status
Peer-reviewed Open access
Awards
CCF-B
World Wide Web · 2022
CAS Zone 4
World Wide Web · 2022
JCR Q2
World Wide Web · 2022
publications

World Wide Web, 2022

Abstract As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.

Jianke Yu
Authors
Jianke Yu (he/him)
PhD (2026) in Graph Machine Learning & Databases
I completed my PhD at the University of Technology Sydney (UTS) in 2026, supervised by Prof. Ying Zhang, A/Prof. Lu Qin and Dr Hanchen Wang. My research develops machine learning algorithms for graph-structured data and database systems — graph neural network architectures, and learning-based methods that improve database and data-mining algorithms (fraud detection, graph similarity, supergraph search, malware detection). My work has appeared in KDD and IEEE TKDE. I am joining Zhejiang Gongshang University as a faculty member in 2027.