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 - Funding Information:
This research work is funded by the National Nature Science Foundation of China under Grant 61971283 and 2020 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Smart energy Internet security situation awareness platform project”. Yuchen Li’s work is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 (Award No.: MOE2019-T2-2-065).
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 -