Influence maximization on temporal networks : a review

Eric Yanchenko*, Tsuyoshi Murata, Petter Holme

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

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Abstract

Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on temporal networks where the network structure changes with time. IM on such dynamically varying networks is the topic of this review. We first categorize methods into two main paradigms: single and multiple seeding. In single seeding, nodes activate at the beginning of the diffusion process, and most methods either efficiently estimate the influence spread and select nodes with a greedy algorithm, or use a node-ranking heuristic. Nodes activate at different time points in the multiple seeding problem, via either sequential seeding, maintenance seeding or node probing paradigms. Throughout this review, we give special attention to deploying these algorithms in practice while also discussing existing solutions for real-world applications. We conclude by sharing important future research directions and challenges.

Original languageEnglish
Article number16
Pages (from-to)1-25
Number of pages25
JournalApplied Network Science
Volume9
Issue number1
DOIs
Publication statusPublished - 21 May 2024
MoE publication typeA2 Review article, Literature review, Systematic review

Keywords

  • Diffusion
  • Dynamic networks
  • Graphs

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