Hunting for Influential Nodes in Complex Networks Using Local Information

Abstract

Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.