TY - GEN
T1 - Attention Network for Information Diffusion Prediction
AU - Wang, Zhitao
AU - Chen, Chengyao
AU - Li, Wenjie
N1 - Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
PY - 2018/4/23
Y1 - 2018/4/23
N2 - In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.
AB - In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.
KW - attention network
KW - information diffusion
UR - http://www.scopus.com/inward/record.url?scp=85074905046&partnerID=8YFLogxK
U2 - 10.1145/3184558.3186931
DO - 10.1145/3184558.3186931
M3 - Conference article published in proceeding or book
AN - SCOPUS:85074905046
T3 - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
SP - 65
EP - 66
BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery, Inc
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -