Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds

TL;DR

This paper proposes a novel community-based approach for selecting influential seed nodes in complex networks by combining network control methods with community structure, improving influence spread efficiency.

Contribution

It introduces a new method that uses community-driven driver nodes for influence maximization, outperforming global driver node selection in synthetic and real networks.

Findings

Community-based driver nodes enhance influence spread.

Community structure exploitation improves seed set effectiveness.

Method outperforms global driver node selection in experiments.

Abstract

Finding a small subset of influential nodes to maximise influence spread in a complex network is an active area of research. Different methods have been proposed in the past to identify a set of seed nodes that can help achieve a faster spread of influence in the network. This paper combines driver node selection methods from the field of network control, with the divide-and-conquer approach of using community structure to guide the selection of candidate seed nodes from the driver nodes of the communities. The use of driver nodes in communities as seed nodes is a comparatively new idea. We identify communities of synthetic (i.e., Random, Small-World and Scale-Free) networks as well as twenty-two real-world social networks. Driver nodes from those communities are then ranked according to a range of common centrality measures. We compare the influence spreading power of these seed sets to the results of selecting driver nodes at a global level. We show that in both synthetic and real networks, exploiting community structure enhances the power of the resulting seed sets.