Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks

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 computational cost. To better understand user engagement in evolving network, we target a new research problem called Anchored Vertex Tracking (AVT) in this paper, 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.