Identifying influential nodes for blocking information cascades based on improved structural holes in social networks

Information cascades are considered to be a major factor in all aspects of disastrous social network phenomenon. Identifying the influential nodes for information cascades is of theoretical and practical significance. Many centrality indexes used to evaluate impact capability of nodes are difficult to balance between location importance and function control. Structural hole nodes have a natural double-edged aspect: they are powerful ways to transmit new information from different clusters, but they are weak at transmitting behaviors that are in some way risky or costly to adopt-transmitting behaviors need to be supported by more neighbors. The SHD centrality based on structural hole theory and degree centrality is proposed which considers the gatekeeper function of structural hole nodes and location importance of high degree nodes. In order to evaluate the effectiveness of SHD centrality, the dynamic SIS model is used to simulate the process of negative information dissemination in four real-world networks. Experiment results show that SHD centrality has a competitive performance in distinguishing the influence of nodes, and SHD method can significantly reduce the speed of information dissemination and limit the scope of negative information dissemination.

I. Introduction

In the era of big data, all kinds of social platforms are rising rapidly, and massive data are having profound impact on people's lifestyle and public opinion [1]. Information cascades are identified as a major factor in almost every disastrous social network phenomenon, e.g., viral marketing, rumor spread, and various negative information dissemination. In complex networks, some nodes play important roles during information dissemination. Identifying influential nodes for blocking information cascades is important significance [2]–[3].