TY - GEN
T1 - Trend detection in social networks using Hawkes processes
AU - Louzada Pinto, Julio Cesar
AU - Chahed, Tijani
AU - Altman, Eitan
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - We develop in this paper a trend detection algorithm, designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between users and topics. We formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the time between the detection and the message broadcasts, the distance between the real broadcast intensity and the maximum expected broadcast intensity, and the social network topology. The proposed trend detection algorithm is simple and uses stochastic control techniques in order to calculate the trend indices. It is also fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of data necessary to the detection.
AB - We develop in this paper a trend detection algorithm, designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between users and topics. We formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the time between the detection and the message broadcasts, the distance between the real broadcast intensity and the maximum expected broadcast intensity, and the social network topology. The proposed trend detection algorithm is simple and uses stochastic control techniques in order to calculate the trend indices. It is also fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of data necessary to the detection.
UR - https://www.scopus.com/pages/publications/84962493250
U2 - 10.1145/2808797.2814178
DO - 10.1145/2808797.2814178
M3 - Conference contribution
AN - SCOPUS:84962493250
T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
SP - 1441
EP - 1448
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
A2 - Pei, Jian
A2 - Tang, Jie
A2 - Silvestri, Fabrizio
PB - Association for Computing Machinery, Inc
T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Y2 - 25 August 2015 through 28 August 2015
ER -