EgoMUIL: Enhancing Spatio-Temporal User Identity Linkage in Location-Based Social Networks With Ego-Mo Hypergraph

Huang, Haojun; Ding, Fengxiang; Yin, Hao; Liu, Gaoyang; Wang, Chen; Wu, Dapeng Oliver

doi:10.1109/tmc.2023.3345312

IEEE Trans. on Mobile Comput.

2024

DOI: 10.1109/tmc.2023.3345312

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EgoMUIL: Enhancing Spatio-Temporal User Identity Linkage in Location-Based Social Networks With Ego-Mo Hypergraph

Haojun Huang,

Fengxiang Ding,

Hao Yin

et al.

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Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

Kim,

Lee,

Kang

et al. 2024

ACM Trans. Knowl. Discov. Data

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The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node’s extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Specifically, TrustSGCN leverages topological information to measure trustworthiness on edge sign for high-order relationships inferred by balance theory. It then considers structural properties inherent to an input network, such as the ratio of triads, to correct for incorrect embedding propagation. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known social theories, i.e. , balance and status, to jointly preserve the edge sign and direction between nodes connected by existing edges in the embedding space. The experiments on six real-world signed network datasets demonstrate that TrustSGCN consistently outperforms six state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN .

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Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

Kim,

Lee,

Kang

et al. 2024

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

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EgoMUIL: Enhancing Spatio-Temporal User Identity Linkage in Location-Based Social Networks With Ego-Mo Hypergraph

Cited by 1 publication

References 51 publications

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

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