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

TL;DR
This paper introduces the Anchored Vertex Tracking problem in dynamic social networks, proposing efficient incremental algorithms to identify critical users over time, which is essential for understanding and influencing user engagement.
Contribution
It formulates the AVT problem in evolving networks, proves its NP-hardness, and develops greedy and incremental algorithms to efficiently track anchored users over time.
Findings
Algorithms outperform baseline methods in efficiency.
Proven effectiveness on real and synthetic datasets.
Significant reduction in computational cost for dynamic networks.
Abstract
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…
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