Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts
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Tong, Vinh Van
Nguyen, Thanh Tam
Jo, Jun
Yin, Hongzhi
Nguyen, Quoc Viet Hung
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Abstract
Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user co-location and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks.
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IEEE Transactions on Knowledge and Data Engineering
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DE200101465
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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Information and computing sciences
Graph, social and multimedia data
Knowledge representation and reasoning
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Huynh, TT; Tong, VV; Nguyen, TT; Jo, J; Yin, H; Nguyen, QVH, Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts, IEEE Transactions on Knowledge and Data Engineering, 2022