Multiple GRAphs-oriented Random wAlk (MulGRA2) for Social Link Prediction

Kuo-Ming Chao

Multiple GRAphs-oriented Random wAlk (MulGRA2) for Social Link Prediction

School of Arts, Humanities, and Social Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users’ check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user’s spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study use the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users’ check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves.

Original language	English
Journal	Information Sciences
Publication status	Published - Apr 2024

Cite this

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title = "Multiple GRAphs-oriented Random wAlk (MulGRA2) for Social Link Prediction",

abstract = "Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users{\textquoteright} check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user{\textquoteright}s spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study use the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users{\textquoteright} check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves.",

author = "Kuo-Ming Chao",

year = "2024",

month = apr,

language = "English",

journal = "Information Sciences",

issn = "0020-0255",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Multiple GRAphs-oriented Random wAlk (MulGRA2) for Social Link Prediction

AU - Chao, Kuo-Ming

PY - 2024/4

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N2 - Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users’ check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user’s spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study use the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users’ check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves.

AB - Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users’ check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user’s spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study use the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users’ check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves.

M3 - Article

SN - 0020-0255

JO - Information Sciences

JF - Information Sciences

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