Bootstrap Deep Metric for Seed Expansion in Attributed Networks

Chunquan Liang, Yifan Wang, Qiankun Chen, Xinyuan Feng, Luyue Wang, Mei Li, Hongming Zhang

Abstract: Seed expansion tasks play an important role in various network applications such as recommendation systems, social network analysis, and bioinformatics. Given a network and a small group of examples as seeds, these tasks involve identifying additional members of interest from the same community. While most existing expansion methods focus on defining a fixed metric function based on the network structure alone, they often overlook the rich content associated with nodes in attributed networks.In this paper, we bridge the gap by learning a deep metric that takes into account both the network structure and node attributes, and by utilizing the recent advanced graph neural networks as encoding functions. The key challenge lies in the extreme scarcity of given positive examples (i.e., the seed nodes) in real-world applications and the absence of negatives (i.e., non-members of the target community). We introduce Bootstrap Deep Metric (BDM), a graph deep metric learning framework for seed expansion problems. BDM utilizes previous versions of representations to generate anchors for positive and unlabeled nodes, and learns enhanced node representations by minimizing the metric losses on both positive and unlabeled nodes. It eliminates the need for negative nodes, while producing closely aligned representations for members of target community and uniformly distributed representations for non-members, which effectively aid in selecting expansion nodes. Experimental results on real-life datasets show that our BDM not only substantially outperforms state-of-the-art approaches but also remarkably surpasses fully labeled classification models in most cases. Codes are available at https://github.com/wangyfnwsuaf/bdm.