Similarity-Aware Sampling for Machine Learning-Based Goal-Oriented Subgraph Extraction

In this paper, we explore and study the research problem of learning an effective algorithm to extract a goal-oriented subgraph, which finds applications in many graph mining scenarios, such as extracting dense/sparse subgraphs and forming effective therapy groups. Specifically, we study the research problem, Similarity-maximized Subgraph Extraction with Minimum Interaction, which aims at extracting a subgraph in which each node has the minimum numbers of neighbors and common neighbors while maximizing the similarity of the selected nodes. We first propose a reinforcement learning-based approach, named RLFG to effectively identify the resulting subgraphs. Then, we observe that directly applying RLFG on large graphs may incur the neighbor explosion problem, which forbids efficient and effective training of the learning model. To address this issue, we propose a sampling strategy with guaranteed performance, named Similarity-aware Subgraph Sampling (SA2S). Experimental results on multiple datasets show that combining our proposed RLFG and SA2S achieves significantly superior performance compared to other state-of-the-art baselines.