Representation Learning for Heterogeneous Information Networks via Embedding Events

29 Jan 2019  ยท  Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao ยท

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new NRL framework, called Event2vec, for HINs to consider both quantities and properties of relations during the representation learning process. Specifically, an event (i.e., a complete semantic unit) is used to represent the relation among multiple objects, and both event-driven first-order and second-order proximities are defined to measure the object relevance according to the quantities and properties of relations. We theoretically prove how event-driven proximities can be preserved in the embedding space by Event2vec, which utilizes event embeddings to facilitate learning the object embeddings. Experimental studies demonstrate the advantages of Event2vec over state-of-the-art algorithms on four real-world datasets and three network analysis tasks (including network reconstruction, link prediction, and node classification).

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction DBLP Event2vec AUC 90.1 # 2
Link Prediction Douban Event2vec AUC 82.3 # 2
Link Prediction IMDb Event2vec AUC 89.4 # 1
Link Prediction Yelp Event2vec AUC 86.2 # 2

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