Details of Research Outputs

Viral marketing on social networks is an important application and hot research problem. Most of the related work on viral marketing focuses on the spread of single information, while a product may associate with multi-attribute in real life. Information on 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 (MIMP). We present the Multi-dimensional IC model (MIC model) for the proposed problem. The objective function for MIMP is proved to be non-submodular, then we solve the problem with the Sandwich Algorithm, which can get a max{f(SU)f¯(SU),f̲(SL∗)f(So∗)}(1-1/e) approximation ratio to the optimal solution. Experiments are conducted in two real world datasets to verify the correctness and effectiveness of the proposed algorithm.