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Pairwise-interactions-based Bayesian Inference of Network Structure from Information Cascades

Published: 30 April 2023 Publication History

Abstract

An explicit network structure plays an important role when analyzing and understanding diffusion processes. In many scenarios, however, the interactions between nodes in an underlying network are unavailable. Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. More specifically, to get more accurate inference results, we measure the weights of each candidate pairwise interaction in different cascades and add them to the likelihood of a contagion process. In addition, a pre-pruning work is introduced for candidate edges to further improve the inference efficiency. Experiments on synthetic and real-world networks show that PBI achieves significantly better results.

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Cited By

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  • (2024)Inferring Information Diffusion Networks without TimestampsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679798(2453-2461)Online publication date: 21-Oct-2024
  • (2024)DANI: fast diffusion aware network inference with preserving topological structure propertyScientific Reports10.1038/s41598-024-82286-x14:1Online publication date: 28-Dec-2024
  • (2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024

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cover image ACM Conferences
WWW '23: Proceedings of the ACM Web Conference 2023
April 2023
4293 pages
ISBN:9781450394161
DOI:10.1145/3543507
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 April 2023

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Author Tags

  1. Bayesian inference
  2. information diffusion.
  3. network inference
  4. survival analysis

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WWW '23
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WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2024)Inferring Information Diffusion Networks without TimestampsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679798(2453-2461)Online publication date: 21-Oct-2024
  • (2024)DANI: fast diffusion aware network inference with preserving topological structure propertyScientific Reports10.1038/s41598-024-82286-x14:1Online publication date: 28-Dec-2024
  • (2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024

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