Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

TL;DR

This paper reviews and categorizes behavior-aware methods for influence maximization in social networks, highlighting approaches that consider user behavior alongside network structure.

Contribution

It provides the first comprehensive survey and taxonomy of influence maximization methods that incorporate user behavior, filling a gap in existing literature.

Findings

Behavior-aware methods are less common than structural approaches.

Taxonomy categorizes influence methods based on behavioral factors.

Highlights potential for improved influence prediction using user behavior.

Abstract

Social networks have become an increasingly common abstraction to capture the interactions of individual users in a number of everyday activities and applications. As a result, the analysis of such networks has attracted lots of attention in the literature. Among the topics of interest, a key problem relates to identifying so-called influential users for a number of applications, which need to spread messages. Several approaches have been proposed to estimate users' influence and identify sets of influential users in social networks. A common basis of these approaches is to consider links between users, that is, structural or topological properties of the network. To a lesser extent, some approaches take into account users' behaviours or attitudes. Although a number of surveys have reviewed approaches based on structural properties of social networks, there has been no comprehensive review of approaches that take into account users' behaviour. This paper attempts to cover this gap by reviewing and proposing a taxonomy of such behaviour-aware methods to identify influential users in social networks.