  [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.
