Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
3-2021
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.
Keywords
Influence Estimation, Graph Neural Networks, Social Networks
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Conference,
First Page
761
Last Page
769
Identifier
10.1145/3437963.3441829
Publisher
ACM
City or Country
Virtual Conference
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.