TY - GEN
T1 - Motif-aware diffusion network inference
AU - Tan, Qi
AU - LIU, Yang
AU - LIU, Jiming
N1 - Funding Information:
Acknowledgment. The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, in part by the grants from the Research Grant Council of Hong Kong SAR under Projects RGC/HKBU12202415 and RGC/HKBU12202417, in part by the the Science and Technology Research and Development Fund of Shenzhen with Project Code JCYJ20170307161544087, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.
PY - 2018
Y1 - 2018
N2 - Characterizing and understanding information diffusion over social networks play an important role in various real-world applications. In many scenarios, however, only the states of nodes can be observed while the underlying diffusion networks are unknown. Many methods have therefore been proposed to infer the underlying networks based on node observations. To enhance the inference performance, structural priors of the networks, such as sparsity, scale-free, and community structures, are often incorporated into the learning procedure. As the building blocks of networks, network motifs occur frequently in many social networks, and play an essential role in describing the network structures and functionalities. However, to the best of our knowledge, no existing work exploits this kind of structural primitives in diffusion network inference. In order to address this unexplored yet important issue, in this paper, we propose a novel framework called Motif-Aware Diffusion Network Inference (MADNI), which aims to mine the motif profile from the node observations and infer the underlying network based on the mined motif profile. The mined motif profile and the inferred network are alternately refined until the learning procedure converges. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of the proposed framework.
AB - Characterizing and understanding information diffusion over social networks play an important role in various real-world applications. In many scenarios, however, only the states of nodes can be observed while the underlying diffusion networks are unknown. Many methods have therefore been proposed to infer the underlying networks based on node observations. To enhance the inference performance, structural priors of the networks, such as sparsity, scale-free, and community structures, are often incorporated into the learning procedure. As the building blocks of networks, network motifs occur frequently in many social networks, and play an essential role in describing the network structures and functionalities. However, to the best of our knowledge, no existing work exploits this kind of structural primitives in diffusion network inference. In order to address this unexplored yet important issue, in this paper, we propose a novel framework called Motif-Aware Diffusion Network Inference (MADNI), which aims to mine the motif profile from the node observations and infer the underlying network based on the mined motif profile. The mined motif profile and the inferred network are alternately refined until the learning procedure converges. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85049379559&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93040-4_50
DO - 10.1007/978-3-319-93040-4_50
M3 - Conference proceeding
AN - SCOPUS:85049379559
SN - 9783319930398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 638
EP - 650
BT - Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
A2 - Webb, Geoffrey I.
A2 - Phung, Dinh
A2 - Ganji, Mohadeseh
A2 - Rashidi, Lida
A2 - Tseng, Vincent S.
A2 - Ho, Bao
PB - Springer Verlag
T2 - 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Y2 - 3 June 2018 through 6 June 2018
ER -