TY - GEN
T1 - Parallel social influence model with Levy flight pattern introduced for large-graph mining on Weibo.com
AU - Wu, Benbin
AU - Yang, Jing
AU - He, Liang
PY - 2013
Y1 - 2013
N2 - With a suitable method to rank the user influence in micro-blogging service, we could get influential individuals to make information reach large populations. Here a novel parallel social influence model is proposed to face to these challenges. In this paper, we firstly propose impact factors named Social Network Centricity and Weibo Heat Trend, describe a general algorithm named ActionRank to calculate the user influence based on these factors and the user-weibo behavior graph. Secondly, we introduce the Levy flight pattern into ActionRank, for the random large distance jumping phenomenon and the power-law distribution of the retweet cascade hops on Weibo.com meet its requirement. Thirdly, the parallel ActionRank is proposed with the help of MapReduce for large-scale graphs. Experiment results demonstrate that ActionRank on Levy flight pattern outperforms other algorithms and show the consistency of parallel ActionRank on datasets with sizes ranging from 20M to 1100 M edges.
AB - With a suitable method to rank the user influence in micro-blogging service, we could get influential individuals to make information reach large populations. Here a novel parallel social influence model is proposed to face to these challenges. In this paper, we firstly propose impact factors named Social Network Centricity and Weibo Heat Trend, describe a general algorithm named ActionRank to calculate the user influence based on these factors and the user-weibo behavior graph. Secondly, we introduce the Levy flight pattern into ActionRank, for the random large distance jumping phenomenon and the power-law distribution of the retweet cascade hops on Weibo.com meet its requirement. Thirdly, the parallel ActionRank is proposed with the help of MapReduce for large-scale graphs. Experiment results demonstrate that ActionRank on Levy flight pattern outperforms other algorithms and show the consistency of parallel ActionRank on datasets with sizes ranging from 20M to 1100 M edges.
UR - https://www.scopus.com/pages/publications/84892870418
U2 - 10.1007/978-3-319-03889-6_12
DO - 10.1007/978-3-319-03889-6_12
M3 - 会议稿件
AN - SCOPUS:84892870418
SN - 9783319038889
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 102
EP - 111
BT - Algorithms and Architectures for Parallel Processing - 13th International Conference, ICA3PP 2013, Proceedings
T2 - 13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013
Y2 - 18 December 2013 through 20 December 2013
ER -