DIN: An efficient algorithm for detecting influential nodes in social graphs using network structure and attributes

Abstract

Detecting influential nodes in social networks represents an essential issue for various applications to identify users that may maximize the influence of information in such networks. Several methods have been proposed to solve this problem often khown as influence maximization problem. However, most of them focused on the structure of the network and ignored the semantic aspect. Besides, these methods are parametric, they require the number k of influential elements in a deterministic manner. In this paper, we propose a parameterless algorithm called DIN (Detecting Influential Nodes in social networks) that combines the structure and the semantic aspect. The main idea of our proposal is to detect communities with overlap, modelize the semantic of each community then select influential elements. Experimental results on computer-generated artificial graphs demonstrate that DIN is efficient for identifying influential nodes, compared with two newly known proposals.