Influence maximization on temporal networks: a review

TL;DR

This review paper discusses influence maximization on temporal networks, categorizing existing methods into single and multiple seeding paradigms, and emphasizes practical deployment and future challenges.

Contribution

It provides a comprehensive categorization and analysis of influence maximization methods on temporal networks, highlighting practical considerations and future research directions.

Findings

Single and multiple seeding paradigms are the main approaches.

Most methods use greedy algorithms or heuristics for influence estimation.

Practical deployment and real-world applications are emphasized.

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.