Finding Critical Users in Social Communities via Graph Convolutions

Finding critical users whose existence keeps a social community cohesive and large is an important issue in social networks. In the literature, such criticalness of a user is measured by the number of followers who will leave the community together when the user leaves. By taking a social community...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2023-01, Vol.35 (1), p.456-468
Main Authors: Zhao, Kangfei, Zhang, Zhiwei, Rong, Yu, Yu, Jeffrey Xu, Huang, Junzhou
Format: Article
Language:English
Subjects:
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Algorithms
Complexity theory
graph neural network
Greedy algorithms
Inference
Neural networks
Nodes
Sampling
Social network analysis
Social networking (online)
Social networks
Task analysis
Testing
Topology
Training
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Summary:Finding critical users whose existence keeps a social community cohesive and large is an important issue in social networks. In the literature, such criticalness of a user is measured by the number of followers who will leave the community together when the user leaves. By taking a social community as a k k -core, which can be computed in linear time, the problem of finding critical users is to find a set of nodes, U U , with a user-given size b b in a k k -core community that maximizes the number of nodes (followers) to be deleted from the k k -core when all nodes in U U are deleted. This problem is known to be NP-hard. In the literature, the state-of-the-art approach, a greedy algorithm is proposed with no guarantee on the set of nodes U U found, since there does not exist a submodular function the greedy algorithm can use to get a better answer iteratively. Furthermore, the greedy algorithm designed is to handle k k -core in any social networks such that it does not consider the structural complexity of a given single graph and cannot get the global optimal by the local optimal found in iterations. In this paper, we propose a novel learning-based approach. Distinguished from traditional experience-based
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3089763