Efficient temporal core maintenance of massive graphs

Bai, Wen; Chen, Yadi; Wu, Di

doi:10.1016/j.ins.2019.11.003

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…The k-core can be computed by using core decomposition algorithm, while the core decomposition is to efficiently compute for each vertex its core number [4]. Besides, with the dynamic change of the graph, incrementally computing the new core number of each affected vertices is known as core maintenance, which has been studied in [1], [3], [15], [23], [28], [35], [36]. Anchored k-core Problem: User engagement in social networks has attracted much attention while quantifying user engagement dynamics in social networks is usually measured by using kcore [5], [7], [11], [20], [24], [31], [32], [34], [37].…”

Section: Related Workmentioning

confidence: 99%

Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks

Cai,

Yang,

et al. 2021

Preprint

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User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among his friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of a whole network. Many user engagement studies have done to find a set of critical (anchored) users in the static social network. However, social network is highly dynamic and its structure is continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs the expensive computational cost. In this paper, we target a new research problem called Anchored Vertex Tracking (AVT) that aims to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored k-core study in the static network. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.

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