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

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(S-U)/(f) over bar (S-U), (f) under bar (S-L(*))/f(S*(o))}(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. (c) 2022 Elsevier B.V. All rights reserved.