Print Email Facebook Twitter LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks Title LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks Author Yang, Dingqi (University of Fribourg; University of Macau) Qu, Bingqing (University of Fribourg) Yang, J. (TU Delft Web Information Systems) Cudré-Mauroux, Philippe (University of Fribourg) Date 2020 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. Subject User mobilitySocial relationshipLocation-based social networkHeterogeneous hypergraphGraph embedding To reference this document use: http://resolver.tudelft.nl/uuid:8c128824-0743-4a0c-8663-8ee6572ac9ba DOI https://doi.org/10.1109/TKDE.2020.2997869 ISSN 1041-4347 Source IEEE Transactions on Knowledge & Data Engineering, 34 (4), 1843-1855 Part of collection Institutional Repository Document type journal article Rights © 2020 Dingqi Yang, Bingqing Qu, J. Yang, Philippe Cudré-Mauroux Files PDF 09099985.pdf 2.8 MB Close viewer /islandora/object/uuid:8c128824-0743-4a0c-8663-8ee6572ac9ba/datastream/OBJ/view