Balanced Influence Maximization in Attributed Social Network Based on Sampling

Abstract

Influence maximization in social networks is the problem of finding a set of seed nodes in the network that maximizes the spread of influence under certain information prorogation model, which has become an important topic in social network analysis. In this paper, we show that conventional influence maximization algorithms cause uneven spread of influence among different attribute groups in social networks, which could lead to severer bias in public opinion dissemination and viral marketing. We formulate the balanced influence maximization problem to address the trade-off between influence maximization and attribute balance, and propose a sampling based solution to solve the problem efficiently. To avoid full network exploration, we first propose an attribute-based (AB) sampling method to sample attributed social networks with respect to preserving network structural properties and attribute proportion among user groups. Then we propose an attributed-based reverse influence sampling (AB-RIS) algorithm to select seed nodes from the sampled graph. The proposed AB-RIS algorithm runs efficiently with guaranteed accuracy, and achieves the trade-off between influence maximization and attribute balance. Extensive experiments based on four real-world social network datasets show that AB-RIS significantly outperforms the state-of-the-art approaches in balanced influence maximization.