A Linear Circuit Model For Social Influence Analysis

TL;DR

This paper introduces a linear circuit-based social influence model that efficiently measures influence, relates to existing methods like PageRank, and improves influence maximization tasks in social networks.

Contribution

The paper develops a novel linear influence model called Circuit, providing efficient influence measurement and influence maximization solutions with theoretical and empirical validation.

Findings

Circuit model outperforms state-of-the-art methods

Reduces computational burden of influence maximization

Provides a physical interpretation of social influence

Abstract

Understanding the behaviors of information propagation is essential for the effective exploitation of social influence in social networks. However, few existing influence models are both tractable and efficient for describing the information propagation process and quantitatively measuring social influence. To this end, in this paper, we develop a linear social influence model, named Circuit due to its close relation to the circuit network. Based on the predefined four axioms of social influence, we first demonstrate that our model can efficiently measure the influence strength between any pair of nodes. Along this line, an upper bound of the node(s)' influence is identified for potential use, e.g., reducing the search space. Furthermore, we provide the physical implication of the Circuit model and also a deep analysis of its relationships with the existing methods, such as PageRank. Then, we propose that the Circuit model provides a natural solution to the problems of computing each single node's authority and finding a set of nodes for social influence maximization. At last, the effectiveness of the proposed model is evaluated on the real-world data. The extensive experimental results demonstrate that Circuit model consistently outperforms the state-of-the-art methods and can greatly alleviate the computation burden of the influence maximization problem.