The harmonic influence in social networks and its distributed computation by message passing

TL;DR

This paper explores a measure of node influence in social networks, proposing a distributed message passing algorithm inspired by electrical network analogies, which converges to approximate influence values even in complex graphs.

Contribution

It introduces a novel distributed algorithm for computing node influence in social networks, extending convergence proofs and applicability to general graph structures.

Findings

Algorithm converges asymptotically on general graphs

Exact influence computation on trees in a number of steps equal to the diameter

Simulations demonstrate the algorithm's practical usefulness

Abstract

In this paper we elaborate upon a measure of node influence in social networks, which was recently proposed by Vassio et al., IEEE Trans. Control Netw. Syst., 2014. This measure quantifies the ability of the node to sway the average opinion of the network. Following the approach by Vassio et al., we describe and study a distributed message passing algorithm that aims to compute the nodes' influence. The algorithm is inspired by an analogy between potentials in electrical networks and opinions in social networks. If the graph is a tree, then the algorithm computes the nodes' influence in a number of steps equal to the diameter of the graph. On general graphs, the algorithm converges asymptotically to a meaningful approximation of the nodes' influence. In this paper we detail the proof of convergence, which greatly extends previous results in the literature, and we provide simulations that illustrate the usefulness of the returned approximation.