Research on seed node mining algorithm in large-scale temporal graph - Xiaohong ZOU; Chengwei XU; Jing CHEN; Biao SONG; Mingyue WANG (Tongxin xuebao)
Most of the existing maximizing influence algorithms based on temporal graph were not applicable for large-scale networks due to the low time efficiency or narrow influence range.Therefore, the seed node mining algorithm named CHG combining heuristic algorithm and greedy strategy was proposed.Firstly, based on the time sequence characteristics of information diffusion in temporal graph, the concept of two-order degree of nodes was given, and the influence of nodes was heuristically evaluated.Secondly, the nodes were filtered according to the influence evaluation results, and the candidate seed node set was constructed.Finally, the marginal effect of candidate seed nodes was calculated to solve the overlap of influence ranges between nodes to ensure the optimal combination of seed nodes.The experiments were carried out on three different scale data sets, and the results show that the proposed algorithm can ensure the high influence of the seed node set even though its running time is relatively shorter.And it can achieve a better trade-off between the time efficiency and the influence range of the seed node set.