@inproceedings{c41a328d5452441f9b7048557e1a5033,
title = "Incremental graph computation: Anchored Vertex Tracking in dynamic social networks (extended abstract)",
abstract = "User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. 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 this paper, we target a new research problem called Anchored Vertex Tracking (AVT), aiming to track the anchored users at each timestamp of evolving networks. To address the AVT problem, 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 demonstrate the performance of our proposed algorithms.",
author = "Taotao Cai and Shuiqiao Yang and Jianxin Li and Sheng, \{Quan Z.\} and Jian Yang and Xin Wang and Zhang, \{Wei Emma\} and Longxiang Gao",
year = "2024",
doi = "10.1109/ICDE60146.2024.00493",
language = "English",
isbn = "9798350317169",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "5723--5724",
booktitle = "2024 IEEE 40th International Conference on Data Engineering ICDE 2024",
address = "United States",
note = "IEEE International Conference on Data Engineering (40th : 2024), ICDE 2024 ; Conference date: 13-05-2024 Through 17-05-2024",
}