TY - GEN
T1 - Leveraging motifs to model the temporal dynamics of diffusion networks
AU - Sarkar, Soumajyoti
AU - Alvari, Hamidreza
AU - Shakarian, Paulo
N1 - Funding Information:
Acknowledgements. Some of the authors are supported through the ARO grant W911NF-15-1-0282.
Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a susceptible node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a node through a network pruning technique that leverages network motifs to identify potential infec-tors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent node of each potentially susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
AB - Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a susceptible node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a node through a network pruning technique that leverages network motifs to identify potential infec-tors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent node of each potentially susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
KW - Information Cascades
KW - Network Motifs
KW - Social Networks
UR - http://www.scopus.com/inward/record.url?scp=85066893505&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066893505&partnerID=8YFLogxK
U2 - 10.1145/3308560.3316703
DO - 10.1145/3308560.3316703
M3 - Conference contribution
AN - SCOPUS:85066893505
T3 - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
SP - 1079
EP - 1086
BT - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
T2 - 2019 World Wide Web Conference, WWW 2019
Y2 - 13 May 2019 through 17 May 2019
ER -