TY - GEN
T1 - Stochastic network motif detection in social media
AU - Liu, Kai
AU - CHEUNG, Kwok Wai
AU - LIU, Jiming
PY - 2011
Y1 - 2011
N2 - Network motifs refer to recurrent patterns of interconnections which are found to be over-represented in real networks when compared with random ones. Such basic building blocks can well characterize the structure of complex networks. Extending the building blocks to stochastic ones allows for more robust motif detection networks which are stochastic in nature. Network motif analysis, commonly adopted in bioinformatics, has recently been applied to also online social media. In this paper, we propose to detect stochastic network motifs in social media with the conjecture that social interactions are of stochastic nature. In particular, we apply a stochastic motif detection algorithm based on the finite mixture model to both synthesized datasets and real online datasets to evaluate the effectiveness. Also, we discuss how the obtained stochastic motifs could be interpreted and compared qualitatively with some of the results obtained from others which are recently reported in the literature.
AB - Network motifs refer to recurrent patterns of interconnections which are found to be over-represented in real networks when compared with random ones. Such basic building blocks can well characterize the structure of complex networks. Extending the building blocks to stochastic ones allows for more robust motif detection networks which are stochastic in nature. Network motif analysis, commonly adopted in bioinformatics, has recently been applied to also online social media. In this paper, we propose to detect stochastic network motifs in social media with the conjecture that social interactions are of stochastic nature. In particular, we apply a stochastic motif detection algorithm based on the finite mixture model to both synthesized datasets and real online datasets to evaluate the effectiveness. Also, we discuss how the obtained stochastic motifs could be interpreted and compared qualitatively with some of the results obtained from others which are recently reported in the literature.
KW - Expectation-maximization algorithm
KW - Mixture model
KW - Social networks
KW - Stochastic network motifs
UR - http://www.scopus.com/inward/record.url?scp=84863154816&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2011.159
DO - 10.1109/ICDMW.2011.159
M3 - Conference proceeding
AN - SCOPUS:84863154816
SN - 9780769544090
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 949
EP - 956
BT - Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
T2 - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Y2 - 11 December 2011 through 11 December 2011
ER -