Influence Maximization on Dynamic Social Networks with Conjugate Learning Automata

TL;DR

This paper extends influence maximization techniques to dynamic social networks using conjugate learning automata, effectively adapting static network algorithms to account for network changes over time.

Contribution

It introduces a novel application of conjugate learning automata to dynamic networks, incorporating network variation into the influence maximization process.

Findings

Outperforms existing methods on synthetic networks

Effective on real-world social network data

Adapts to network dynamics by learning from influence range variation

Abstract

Selecting the optimal subset from all vertices as seeds to maximize the influence in a social network has been a task of interest. Various methods have been proposed to select the optimal vertices in a static network, however, they are challenged by the dynamics, i.e. the time-dependent variation of the social network structure. Such dynamics hinder the paradigm for static networks and leaves a seemingly unbridgeable gap between algorithms of influence maximization on static networks and those on dynamic ones. In this paper, we extend our previous work and demonstrate that conjugate learning automata (an elementary variant of reinforcement learning) that have been successfully applied to maximize influence on static networks can be applied to dynamic networks as well. The network dynamics is measured by the variation of the influence range and absorbed into the learning procedure. Our proposal delicately formulates the effect of network dynamics: the more the influence range varies, the more likely the seeds are to be learned from scratch. Under this assumption, the continuity of the network variation is fully taken advantage of. Experimental results on both synthetic and real-world networks verify the privileges of our proposal against alternative methods.