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

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, such as spreading messages, viral marketing or preventing rumour propagation. 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.

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

“…The only difference here was that some new elements have been added, including the latest methods, parallel or distributed computing, xx:5 theoretical analysis and comparison, etc. In [216] [25], they considered two kinds of typical variant IM problems, behaviour-aware and location-aware IM problems. They summarized some updated research progress and challenges in this direction.…”