LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks

Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudré-Mauroux

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
181 Downloads (Pure)

Abstract

Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users' mobility homophily and social proximity, and show that mobility and social features can help friendship and location prediction tasks, respectively. However, these hand-crafted features not only require tedious human efforts, but also are difficult to generalize. Against this background, we propose in this paper LBSN2Vec++, a heterogeneous hypergraph embedding approach designed specifically for LBSN data for automatic feature learning. Specifically, LBSN data intrinsically forms a heterogeneous hypergraph including both user-user homogeneous edges (friendships) and user-time-POI-semantic heterogeneous hyperedges (check-ins). Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by not only preserving the nn-wise node proximity captured by the hyperedges, but also considering embedding space transformation between node domains to fully grasp the complex structural characteristics of the LBSN heterogeneous hypergraph. Using real-world LBSN datasets collected in six cities all over the world, our extensive evaluation shows that LBSN2Vec++ significantly and consistently outperforms both state-of-the-art graph embedding techniques by up to 68 percent and the best-performing hand-crafted features in the literature by up to 70.14 percent on friendship and location prediction tasks.

Original languageEnglish
Pages (from-to)1843-1855
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • User mobility
  • Social relationship
  • Location-based social network
  • Heterogeneous hypergraph
  • Graph embedding

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