Abstract: Influence maximization (IM) has been extensively studied in the analysis and mining of social networks, aiming to find a seed set with k nodes to maximize the coverage of influence spread under a specific propagation model. The current studies rarely consider the influence of network structure on information propagation, and IM algorithms are typically not adaptive to networks with various structures. To solve this problem, this paper studies the IM problem with network structure adaptability, and analyzes the influence of network structure on information propagation. Firstly, according to the relation between network structure and propagation process, three allocation strategies are proposed to adapt to different network types. Secondly, with the influence of nodes measured at the community scale, the initial seed nodes set is constructed. Finally, the initial set of seed nodes is adjusted and optimized to further improve the quality of the seed nodes. Experiments on real and synthetic datasets with different structures show that the proposed algorithm achieves better performance. The paper discovers that the relation between influence spread and the average distance between seed nodes is not that the greater the distance, the better the influence spread, which changes the inherent perception of the average distance between seed nodes when considering the problem of propagation overlap.

Key words: social network, influence maximization, network structure adaptability, allocation strategy, community structure