Temporal Link Prediction Methods Based on Behavioral Synchrony

Yueran Duan, Qing Guan*, Petter Holme, Yacheng Yang, Wei Guan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

Link prediction—to identify potential missing or spurious links in temporal network data—has typically been based on local structures, ignoring long-term temporal effects. In this chapter, we propose link-prediction methods based on agents’ behavioral synchrony. Since synchronous behavior signals similarity and similar agents are known to have a tendency to connect in the future, behavioral synchrony could function as a precursor of contacts and, thus, as a basis for link prediction. We use four data sets of different sizes to test the algorithm’s accuracy. We compare the results with traditional link prediction models involving both static and temporal networks. Among our findings, we note that the proposed algorithm is superior to conventional methods, with the average accuracy improved by approximately 2–5%. We identify different evolution patterns of four network topologies—a proximity network, a communication network, transportation data, and a collaboration network. We found that: (1) timescale similarity contributes more to the evolution of the human contact network and the human communication network; (2) such contribution is not observed through a transportation network whose evolution pattern is more dependent on network structure than on the behavior of regional agents; (3) both timescale similarity and local structural similarity contribute to the collaboration network.
Original languageEnglish
Title of host publicationTemporal Network Theory
EditorsPetter Holme, Jari Saramäki
PublisherSpringer
Pages381-402
Edition2
ISBN (Electronic)978-3-031-30399-9
ISBN (Print)978-3-031-30398-2
DOIs
Publication statusPublished - 2023
MoE publication typeA3 Book section, Chapters in research books

Publication series

NameComputational Social Sciences
PublisherSpringer
ISSN (Print)2509-9574
ISSN (Electronic)2509-9582

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