TY - GEN
T1 - Information diffusion pattern mining over online social media
AU - Lu, Hsueh-Chan
AU - Hung, Hui Ju
PY - 2014/1/1
Y1 - 2014/1/1
N2 - With the rapid development of Web 2.0 technology, online social media have become increasingly popular and influential. In online social media, such as Reddit, Digg, Twitter and Weibo, users can post, vote and comment posted stories and other users’ comments. Users, together with story and corresponding feedbacks, form a heterogeneous information diffusion network. To analyze how information diffuses among different users, we need to better understand a few key factors, including (1) frequent appearing sub-structures, called motifs, in the network and (2) evolution of a motif. In this paper, we explore the MOtif-based Sequential Pattern (MOSP) to facilitate the understanding of motif evolution along the time. Furthermore, we propose Topological MOSP (T-MOSP) and Propagative MOSP (P-MOSP) to observe frequent sequence of motifs in different angles. Facing a large volume of graph data, Motif mining is time-consuming. Therefore, we devise efficient mining algorithms, namely, Motif-Mine and Lattice-based Temporal Sequential Pattern Mine (LTSP-Mine), to discover motifs and sequences of motifs, respectively. Extensive experimental evaluation on Digg demonstrates that T-MOSP and P-MOSP discovered by the proposed algorithms can efficiently and effectively capture and summarize the information diffusion patterns in online social media.
AB - With the rapid development of Web 2.0 technology, online social media have become increasingly popular and influential. In online social media, such as Reddit, Digg, Twitter and Weibo, users can post, vote and comment posted stories and other users’ comments. Users, together with story and corresponding feedbacks, form a heterogeneous information diffusion network. To analyze how information diffuses among different users, we need to better understand a few key factors, including (1) frequent appearing sub-structures, called motifs, in the network and (2) evolution of a motif. In this paper, we explore the MOtif-based Sequential Pattern (MOSP) to facilitate the understanding of motif evolution along the time. Furthermore, we propose Topological MOSP (T-MOSP) and Propagative MOSP (P-MOSP) to observe frequent sequence of motifs in different angles. Facing a large volume of graph data, Motif mining is time-consuming. Therefore, we devise efficient mining algorithms, namely, Motif-Mine and Lattice-based Temporal Sequential Pattern Mine (LTSP-Mine), to discover motifs and sequences of motifs, respectively. Extensive experimental evaluation on Digg demonstrates that T-MOSP and P-MOSP discovered by the proposed algorithms can efficiently and effectively capture and summarize the information diffusion patterns in online social media.
UR - https://www.scopus.com/pages/publications/84915821413
UR - https://www.scopus.com/pages/publications/84915821413#tab=citedBy
U2 - 10.1007/978-3-319-13186-3_13
DO - 10.1007/978-3-319-13186-3_13
M3 - Conference contribution
AN - SCOPUS:84915821413
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 148
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
A2 - Peng, Wen-Chih
A2 - Wang, Haixun
A2 - Zhou, Zhi-Hua
A2 - Ho, Tu Bao
A2 - Tseng, Vincent S.
A2 - Chen, Arbee L.P.
A2 - Bailey, James
PB - Springer Verlag
T2 - International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
Y2 - 13 May 2014 through 16 May 2014
ER -