SURREAL: Subgraph Robust Representation Learning

Applied Network Science

Evangelos E. Papalexakis¹  Danai Koutra²  Saba A. Al-Sayouri³  Sarah S. Lam³ 

[1] Computer Science and Engineering Department, University of California, Riverside;Computer Science and Engineering Department, University of Michigan;Systems Science and Industrial Engineering Department, Binghamton University;

关键词: Large-scale information networks;    Representation learning;    Graph embeddings;    Graph embeddings;   

DOI  :  10.1007/s41109-019-0160-1

来源: DOAJ

【 摘 要 】

Abstract The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhoods. However, many existing network representation learning methods generate embeddings that are still not effective enough, or lead to unstable representations due to random processes (e.g., random walks to generate context) and thus, cannot generalize to multi-graph problems. In this paper, we propose SURREAL, a novel, stable graph embedding algorithmic framework that leverages “spatio-electric” (SE) subgraphs: it learns graph representations using the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes that may incidentally connect a pair of nodes in a graph. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art techniques by up to 37% (6% on average) on different multi-label classification problems. Further, in contrast to baseline methods, SURREAL, being deterministic, is stable and thus can generalize to single and multi-graph tasks.

【 授权许可】

Unknown