Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model.

本研究提出了Cascade Bipartite Graph Neural Networks——一种新颖的自监督节点表示学习方法，该方法可以有效地和高效地在独特的双边图结构中聚合信息，并经过多个数据集的实验验证其高效性和实用性。

Cascade-BGNN：面向大规模双分图的有效自监督表示学习