TY - GEN
T1 - DeepIS
T2 - 14th ACM International Conference on Web Search and Data Mining, WSDM 2021
AU - Xia, Wenwen
AU - Li, Yuchen
AU - Wu, Jun
AU - Li, Shenghong
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/8/3
Y1 - 2021/8/3
N2 - Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural networks (GNNs) for predicting susceptibility. As GNNs aggregate multi-hop neighbor information and could generate over-smoothed representations, the prediction quality for susceptibility is undesirable. To address the shortcomings of GNNs for susceptibility estimation, we propose a novel DeepIS model with a two-step approach: (1) a coarse-grained step where we estimate each node's susceptibility coarsely; (2) a fine-grained step where we aggregate neighbors' coarse-grained susceptibility estimations to compute the fine-grained estimate for each node. The two modules are trained in an end-to-end manner. We conduct extensive experiments and show that on average DeepIS achieves five times smaller estimation error than state-of-the-art GNN approaches and two magnitudes faster than Monte Carlo simulation.
AB - Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural networks (GNNs) for predicting susceptibility. As GNNs aggregate multi-hop neighbor information and could generate over-smoothed representations, the prediction quality for susceptibility is undesirable. To address the shortcomings of GNNs for susceptibility estimation, we propose a novel DeepIS model with a two-step approach: (1) a coarse-grained step where we estimate each node's susceptibility coarsely; (2) a fine-grained step where we aggregate neighbors' coarse-grained susceptibility estimations to compute the fine-grained estimate for each node. The two modules are trained in an end-to-end manner. We conduct extensive experiments and show that on average DeepIS achieves five times smaller estimation error than state-of-the-art GNN approaches and two magnitudes faster than Monte Carlo simulation.
KW - graph neural networks
KW - influence estimation
KW - social networks
UR - http://www.scopus.com/inward/record.url?scp=85103052497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103052497&partnerID=8YFLogxK
U2 - 10.1145/3437963.3441829
DO - 10.1145/3437963.3441829
M3 - Conference contribution
AN - SCOPUS:85103052497
T3 - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
SP - 761
EP - 769
BT - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 8 March 2021 through 12 March 2021
ER -