Influence maximization (IM) has been extensively researched in the information propagation field and applied in various domains. However, existing studies on the IM have primarily focused on network structure, and lack the in-depth exploration of online network complexities, like personal history or preference. In this paper, a heterogeneous cross-comparison improved Kshell algorithm (HCCKshell) is proposed to solve IM, applying users’ multi-dimensional attributes in the propagation, including social history and topological structure . Specifically, the model learns users’ potential representation of historical content preferences and topological structure based on the Encoder and GCN thoughts, then defines the heterogeneous similarity and the heterogeneous information entropy to measure users’ influence ability and provide reliability assurance on the propagation. To improve the performance, a cross-comparison improved K-shell heuristic algorithm based on the heterogeneous information entropy is proposed to find a valid influential seed set. Furthermore, the experiments on multiple real large-scale datasets and their results indicate that our HCCKshell algorithm is more effective than baseline algorithms on both effect and performance.

Most research on influence maximization focuses on the network structure features of the diffusion process but lacks the consideration of multi-dimensional characteristics. This paper proposes the attributed influence maximization based on the crowd emotion, aiming to apply the user’s emotion and group features to study the influence of multi-dimensional characteristics on information propagation. To measure the interaction effects of individual emotions, we define the user emotion power and the cluster credibility, and propose a potential influence user discovery algorithm based on the emotion aggregation mechanism to locate seed candidate sets. A two-factor information propagation model is then introduced, which considers the complexity of real networks. Experiments on real-world datasets demonstrate the effectiveness of the proposed algorithm. The results outperform the heuristic methods and are almost consistent with the greedy methods yet with improved time performance.

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.