Influential Node Ranking in Complex Information Networks Using A Randomized Dynamics-Sensitive Approach

TL;DR

This paper introduces a dynamic, randomized approach for ranking influential nodes in information networks, which adapts to changes in diffusion processes and outperforms static methods in correlation and uniqueness.

Contribution

The proposed method uses random sub-graphs and hyper-graph representations to approximate influentiality, offering a dynamic and more accurate ranking approach.

Findings

Achieves highest correlation with ground-truth rankings

Generates rankings with highest uniqueness and uniformity

Maintains competitive running time compared to state-of-the-art methods

Abstract

Identifying the most influential nodes in information networks has been the focus of many research studies. This problem has crucial applications in various contexts, such as controlling the propagation of viruses or rumours in real-world networks. While existing approaches mostly rely on the structural properties of networks and generate static rankings, in this work we propose a novel method that is responsive to any change in the diffusion dynamics. The main idea is to approximate the influential ability (influentiality) of a node with the reachability of other nodes from that node in a set of random sub-graphs. To this end, several random sub-graphs are sampled from the original network and then a hyper-graph is created in which each sub-graph is represented with a hyper-edge. From a theoretical standpoint, one can argue that a factor of the degree of nodes in the hyper-graph approximates influentiality. From an empirical perspective, the proposed model not only achieves the highest correlation with the ground-truth ranking, but also the ranking generated by this method hits the highest level of uniqueness and uniformity. Theoretical and practical analysis of the running time of this method also confirms a competitive running time compared with state-of-the-art methods.