DeepIS: Susceptibility Estimation on Social Networks

Wenwen Xia, Yuchen Li, Jun Wu, Shenghong Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages761-769
Number of pages9
ISBN (Electronic)9781450382977
DOIs
Publication statusPublished - 2021 Aug 3
Externally publishedYes
Event14th ACM International Conference on Web Search and Data Mining, WSDM 2021 - Virtual, Online, Israel
Duration: 2021 Mar 82021 Mar 12

Publication series

NameWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Conference

Conference14th ACM International Conference on Web Search and Data Mining, WSDM 2021
Country/TerritoryIsrael
CityVirtual, Online
Period21/3/821/3/12

Keywords

  • graph neural networks
  • influence estimation
  • social networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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