Multi-attribute based influence maximization in social networks: Algorithms and analysis

Abstract

The most valuable feature of social networks is that they can generate contents for users and spread them quickly on the network, which is a very important platform for viral marketing. Most of the related work on viral marketing focuses on the spread of single information, while a product may associate with multiple attributes in real life. Information about multiple attributes of a product propagates in the social networks simultaneously and independently. The attribute information that a user receives will determine whether he would purchase the product or not. We extend the traditional single information influence maximization problem to the multi-attribute based influence maximization problem. We also present the Multi-dimensional IC model (MIC model) for the proposed problem, then formulate the problem as the Multi-attribute based Influence Maximization Problem (MIMP). The objective function for MIMP is proved to be non-submodular, then we solve the problem with two different algorithms: the Sandwich Algorithm and the Supermodular Algorithm, whose solutions can get a max{f(SU)f‾(SU),f_(SL⁎)f(So⁎)}(1−1/e) approximation ratio and an 1/(d+2) approximation ratio to the optimal solution, respectively. Experiments based on the real world social network datasets verify the effectiveness and correctness of our proposed solutions.