Effects of Network Communities and Topology Changes in Message-Passing Computation of Harmonic Influence in Social Networks
For researchers studying influence measures in social networks, this work highlights limitations of the harmonic influence algorithm in community-structured networks.
The paper investigates the performance of a distributed message-passing algorithm for computing harmonic influence in social networks, finding that community structures cause overestimation of local leaders' importance, while the algorithm adapts smoothly to topology changes.
The harmonic influence is a measure of the importance of nodes in social networks, which can be approximately computed by a distributed message-passing algorithm. In this extended abstract we look at two open questions about this algorithm. How does it perform on real social networks, which have complex topologies structured in communities? How does it perform when the network topology changes while the algorithm is running? We answer these two questions by numerical experiments on a Facebook ego network and on synthetic networks, respectively. We find out that communities can introduce artefacts in the final approximation and cause the algorithm to overestimate the importance of "local leaders" within communities. We also observe that the algorithm is able to adapt smoothly to changes in the topology.