Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
By Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudré-Mauroux. The Web Conference, 2019
334 citations recorded by the source corpus.
Location Based Social Networks (LBSNs) have been widely used as a primary data source to study 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. In this paper, by revisiting user mobility and social relationships based on a large-scale LBSN dataset collected over a long-term period, we propose LBSN2Vec, a hypergraph embedding approach designed specifically for LBSN data for automatic feature learning. Specifically, LBSN data intrinsically forms a hypergraph including both user-user edges (friendships) and user-time-POI-semantic 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 preserving n-wise node proximity (n = 2 or 4). Our evaluation results show that LBSN2Vec both consistently and significantly outperforms the state-of-the-art graph embedding methods on both friendship and location prediction tasks, with an average improvement of 32.95% and 25.32%, respectively. Moreover, using LBSN2Vec, we discover the asymmetric impact of mobility and social relationships on predicting each other, which can serve as guidelines for future research on friendship and location prediction in LBSNs.
Subfields: Artificial Intelligence, Information Systems, Transportation
View Paper: https://www.semanticscholar.org/paper/7793d888d82625dc34432110173d5a5d43f5c983
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hongzhi Yin
- Huan Liu
- Jundong Li
- Junliang Yu
- Kaize Ding
- Xiangliang Zhang
- Nguyen Quoc Viet Hung
- James Caverlee
In Information Systems
- Hongzhi Yin
- Junliang Yu
- Huan Liu
- Xiangliang Zhang
- Jundong Li
- Nguyen Quoc Viet Hung
- James Caverlee
- Qinyong Wang
In Transportation
- Gianni Barlacchi
- Luca Pappalardo
- Hongzhi Yin
- Huan Liu
- Massimiliano Luca
- Bruno Lepri
- Charu C. Aggarwal
- Bryan Perozzi
Citations in the corpus
- A Survey on Deep Learning for Human Mobility (2020, methodology): “DeepJMT is evaluated using ACC@k on Foursquare check-ins in New York City [218], Tokyo [218], and Istanbul.”
- Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “LBSN2Vec [47] is a social-aware POI recommendation model that builds hyperedges by jointly sampling friendships and check-ins with random walk, but it focuses on connecting different types of entities instead of exploiting the high-order social network structures.”
- Next-item Recommendation with Sequential Hypergraphs (2020, background): “In terms of modeling the correlations among various types of objects, there are early efforts [5, 21, 43, 45] in applying hypergraphs to assist conventional collaborative filtering for incorporating context information.”