Information Coverage Maximization in Social Networks

TL;DR

This paper introduces the problem of maximizing total information coverage, including both active and informed nodes, in social networks, and proposes algorithms with validated experimental performance.

Contribution

It formulates the new problem of information coverage maximization, proves its NP-hardness and submodularity, and develops algorithms with experimental validation.

Findings

Algorithms outperform baseline methods in coverage.

The problem is NP-hard but admits efficient approximation.

Experimental results on real datasets confirm effectiveness.

Abstract

Social networks, due to their popularity, have been studied extensively these years. A rich body of these studies is related to influence maximization, which aims to select a set of seed nodes for maximizing the expected number of active nodes at the end of the process. However, the set of active nodes can not fully represent the true coverage of information propagation. A node may be informed of the information when any of its neighbours become active and try to activate it, though this node (namely informed node) is still inactive. Therefore, we need to consider both active nodes and informed nodes that are aware of the information when we study the coverage of information propagation in a network. Along this line, in this paper we propose a new problem called Information Coverage Maximization that aims to maximize the expected number of both active nodes and informed ones. After we prove that this problem is NP-hard and submodular in the independent cascade model and the linear threshold model, we design two algorithms to solve it. Extensive experiments on three real-world data sets demonstrate the performance of the proposed algorithms.