Diffusion models and approaches for influence maximization in social networks

Abstract

Social Network Analysis (SNA) deals with studying the structure, relationship and other attributes of social networks, and provides solutions to real world problems. Influence maximization is one of the significant areas in SNA as it helps in finding influential entities in online social networks which can be used in marketing, election campaigns, outbreak detection, and so on. It deals with the problem of finding a subset of nodes called seeds such that it will eventually spread maximum influence in the network. This paper focuses on providing a complete survey on the influence maximization problem and covers three major aspects: i) different types of input required ii) influence propagation models that map the spread of influence in the network, and iii) the approximation algorithms suggested for seed set selection. We also provide the state of the art and describe the open problems in this domain. � 2016 IEEE.