Logarithmic Gravity Centrality for Identifying Influential Spreaders in Dynamic Large-scale Social Networks

The task of identifying influential spreaders for various big data social network applications plays a crucial role in social networks, and lays the foundation for predictive or recommended applications. Though there are several kinds of methods for this task, most of these methods exploit global computing, and are time-consuming for large-scale social networks. In this paper, by combining the degree centrality with the law of universal gravitation in physics, we present a novel metric called Logarithm Gravity (LG) centrality to quantify the influence of nodes in large-scale social networks, which views the value of the degree centrality as mass for each node and regards the length of the shortest path between a pair of nodes as their distance. In our model, for each node, a local network is generated by obtaining all nodes, which are less than k-hop from it. Then the sum of mutual influence values between the node in question and all other nodes in each local network is figured out as its LG centrality index. Therefore, the complexity of our approach is scalable by adjusting the value of k with efficient local computation. We compare our LG centrality with k-shell, betweenness and degree centralities. Experimental evidence, which has been collected based on the SIR model with four real-world datasets, shows that our approach is more feasible and effective than other state-of-art methods in terms of infection ratios and computational complexity.