Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization

Abstract

This paper presents the identification of highly influential nodes based on temporal-aware modeling of multi-hop neighbor interactions to maximize the spread of information in online social networks (OSNs). The objective is to choose a set of influential nodes that have higher temporal multi-hop interactions and more topological connections in large-scale OSNs to maximize information dissemination and minimize spreading time. An influence diffusion process that is solely based on topology is not able to capture the influence spreading efficiently. A temporal multi-hops social interaction based centrality is proposed to choose nodes of higher spreading ability considering the nodes’ neighbors and neighbors-of-neighbors temporal modeled interactions and topological connections. The temporal-aware interactions are modeled to find users who are more active recently. First, we model the influence between users considering the temporal interactions of the user and its neighbors. A subset of nodes with a higher influence value and more topological connections with direct neighbors is selected. Secondly, we select the Top-K higher influential spreader nodes from the subset of nodes considering the node neighbors and neighbors-of-neighbors temporal modeled social interactions and topological connections. Finally, the proposed algorithm is evaluated using the epidemic spreading models. The experimental results show that the algorithm is able to extract highly influential nodes that maximize the spread of information and minimize contagion time.