Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks

Junzhou Zhao, Shuo Shang, Pinghui Wang, John C.S. Lui, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods.
Original languageEnglish (US)
Title of host publication2019 IEEE 35th International Conference on Data Engineering (ICDE)
PublisherIEEE
Pages1106-1117
Number of pages12
ISBN (Print)9781538674741
DOIs
StatePublished - Jun 6 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank the anonymous reviewers for their valuable comments and suggestions to help us improve this paper. This work is financially supported by the King Abdullah University of Science and Technology (KAUST) Sensor Initiative, Saudi Arabia. The work of John C.S. Lui was supported in part by the GRF Funding 14208816.

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