EIOA: A computing expectation-based influence evaluation method in weighted hypergraphs

Influence maximization (IM) is a key issue in network science. However, previous research on IM has previously explored binary interaction relationship in ordinary graphs, with little consideration for higher-order interaction that are more practical in hypergraphs, especially weighted hypergraphs. Therefore, this study focuses on solving the IM problem in weighted hypergraphs. Firstly, we adopt a novel and more reasonable dissemination model, namely the adaptive dissemination (AD), and incorporate it into weighted hypergraphs. Next, a computing expectation-based influence evaluation method is proposed to accurately obtain the expected influence in one-hop area (EIOA) of the seed node set. Meanwhile, three search algorithms are designed using the EIOA to effectively solve the initial seed set. Then, multi-level experiments are conducted to compare the proposed algorithms with other six advanced algorithms in eight weighted hypergraph datasets from the real world. The experimental results are visually analyzed and two nonparametric test processes are applied to verify the significant advantages of the proposed algorithms. Finally, the impact of different factors such as seed set correlation, model parameter setting, and weight attribute on dissemination is explored, and the efficiency and robustness of these algorithms are further validated.