Potential-Driven Model for Influence Maximization in Social Networks

Zineb Felfli,Khalil Shujaee,Mohamed Kerwat,Roy George

doi:10.1109/access.2020.3029442

Abstract

The adoption of a new technology, the spreading of ideologies, the transmission of contagions, are examples of spreading phenomena that can be modelled at two distinct levels of resolution, a macroscopic and a microscopic level.In the present work, we consider the network at the microscopic level and represent its nodes as a system of interacting particles and propose a novel, physics-inspired approach. This approach assumes that nodes interact via `forces' that derive from the `potential' that each node creates at the location of the other nodes, leading to a potential gradient that indicates the `natural' direction of diffusion through the network. A set of influencers in the network, is determined from strategically selected nodes based on the value of their net potential. We use synthetic networks of various sizes and compare the influence spread resulting from a seed set determined from the potential-based model and alternate approaches, including the greedy algorithm of Kempe et al. Our findings indicate that this approach achieves comparable results to those of the greedy algorithm without the prohibitive computational cost, and consistently outperforms the other approaches by a large margin. We then apply our methodology to information spreading related to the Twitter Higgs Boson user dataset. The results are analyzed in the context of influence maximization, and we provide insights into the general application of this technique for information dissemination.

Highlights

Preliminary work on diffusion, mainly in anthropological and social sciences [1] can be traced back to the turn of the 20th century
[17] Kempe et al compare the performance of their greedy algorithm to that of node selection heuristics based on degree and distance centrality traditionally used in sociology as a measure for a node’s influence [38]
To assess the quality and validity of our approach, here we consider results obtained with the greedy algorithm as the standard to which all results are compared, namely results obtained with our potential-based approach, degree and distance centrality as well as those using randomly selected nodes as a seed set

Summary

Introduction

Preliminary work on diffusion, mainly in anthropological and social sciences [1] can be traced back to the turn of the 20th century. [17] Kempe et al compare the performance of their greedy algorithm to that of node selection heuristics based on degree and distance centrality traditionally used in sociology as a measure for a node’s influence [38].

Results

Conclusion