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with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable.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.
Highlights
Graph-structured data is becoming increasingly ubiquitous, especially with the spreading popularity of the e-commerce platforms and social networks [32, 33]
Our proposed Polarity-based Graph Convolutional Network (PbGCN) aims to preserve the universal relationship between neighboring nodes, achieves higher accuracy in the sign prediction task for signed bipartite graphs compared to the state-of-the-art heuristic methods
The signed bipartite graphs are becoming more and more ubiquitous in real-life applications, but few research works are conducted due to the complexities brought by the signed links and bipartite settings
Summary
Graph-structured data is becoming increasingly ubiquitous, especially with the spreading popularity of the e-commerce platforms and social networks [32, 33] These networks can be modeled as signed graphs whose edges have either positive or negative signs. With the growing popularity of graph neural networks (GNNs), a variety of network embedding and GNN-based methods are developed for unipartite signed graphs and unsigned bipartite graphs These models lack the capability to fully preserve the information of negative links and node partitions. GNNs designed for unsigned bipartite graphs [9, 21] will aggregate the neighbor information in the same way for both positive and negative edges As a result, they totally ignore the sign information and cannot be used for the sign prediction task.
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