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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 \u201cspatio-electric\u201d (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.<\/jats:p>","DOI":"10.1007\/s41109-019-0160-1","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T17:32:39Z","timestamp":1571851959000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SURREAL: Subgraph Robust Representation Learning"],"prefix":"10.1007","volume":"4","author":[{"given":"Saba A.","family":"Al-Sayouri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danai","family":"Koutra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelos E.","family":"Papalexakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah S.","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"160_CR1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1137\/1.9781611972832.5","volume-title":"Proceedings of the 2013 SIAM International Conference on Data Mining","author":"L Akoglu","year":"2013","unstructured":"Akoglu, L, Chau DH, Vreeken J, Tatti N, Tong H, Faloutsos C (2013) Mining Connection Pathways for Marked Nodes in Large Graphs In: Proceedings of the 2013 SIAM International Conference on Data Mining, 37\u201345.. 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