Abstract
Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding utilize only the topology information, while recently several methods are proposed to consider node content information. However, the copious information on edges has not been explored. In this paper, we study the problem of representation learning in node/edge attributed graph, which differs from normal attributed graph in that edges can also be contented with attributes. We propose GERI, which learns graph embedding with rich information in node/edge attributed graph through constructing a heterogeneous graph. GERI includes three steps: construct a heterogeneous graph, take a novel and biased random walk to explore the constructed heterogeneous graph and finally use modified heterogeneous skip-gram to learn embedding. Furthermore, we upgrade GERI to semi-supervised GERI (named SGERI) by incorporating label information on nodes. The effectiveness of our methods is demonstrated by extensive comparison experiments with strong baselines on various datasets.
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This work is supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639.
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Sun, G., Zhang, X. (2019). A Novel Framework for Node/Edge Attributed Graph Embedding. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_14
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