[Background]Discovering key nodes plays a significant role in Social Network Analysis(SNA). Effective and accurate mining of key nodes promotes more successful applications in fields like advertisement and recommendation. [Methods] With focus on the temporal and categorical property of users' actions - when did they re-tweet or reply a message, as well as their social intimacy measured by structural embeddings, we designed a more sensitive PageRank-like algorithm to accommodate the growing and changing social network in the pursue of mining key nodes. [Results] Compared with our baseline PageRank algorithm, key nodes selected by our ranking algorithm noticeably perform better in the SIR disease simulations with SNAP Higgs dataset. [Conclusion] These results contributed to a better understanding of disseminations of social events over the network.
@article{arxiv.1802.10083,
title = {Discovering Key Nodes in a Temporal Social Network},
author = {Jinshuo Liu and Chenghao Mou and Donghong Ji},
journal= {arXiv preprint arXiv:1802.10083},
year = {2018}
}