Incremental graph computation: anchored vertex tracking in dynamic social networks

Taotao Cai, Shuiqiao Yang, Jianxin Li*, Quan Z. Sheng, Jian Yang, Xin Wang, Wei Emma Zhang, Longxiang Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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 her 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 the whole network. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are 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 an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem called Anchored Vertex Tracking (AVT) in this article, aiming 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 networks. 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.

Original languageEnglish
Pages (from-to)7030-7044
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number7
Early online date17 Aug 2022
Publication statusPublished - Jul 2023


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