The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
Open accessAbstract
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.
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Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416
A New Status Index Derived from Sociometric Analysis
Katz, L. (1953) · Psychometrika · doi:10.1007/bf02289026 · Cited by 3,866
The link‐prediction problem for social networks
Liben‐Nowell, D., & Kleinberg, J. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,043
Friends and neighbors on the Web
Adamic, L. A., & Adar, E. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00009-1 · Cited by 2,954
Friendship and mobility: user movement in location-based social networks
Cho, E., Myers, S. A., & Leskovec, J. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020579 · Cited by 2,903
Link prediction in complex networks: A survey
Lü, L., & Zhou, T. (2011) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,819
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
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Metadata
- Authors
- Yang, D., Qu, B., Yang, J., & Cudre-Mauroux, P.
- Venue
- The World Wide Web Conference
- Publisher
- ACM
- Published
- 2019-05-13
- Pages
- 2147-2157
- OpenAlex
- W2913696439
- Cited by
- 299
- References
- 39
- FWCI
- 88.22
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Yang, D., Qu, B., Yang, J., & Cudre-Mauroux, P. (2019). Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach. The World Wide Web Conference. https://doi.org/10.1145/3308558.3313635
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