research-article

Multi-aspect Diffusion Network Inference

Authors:

Ting GanAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 82 - 90

https://doi.org/10.1145/3543507.3583228

Published: 30 April 2023 Publication History

Abstract

To learn influence relationships between nodes in a diffusion network, most existing approaches resort to precise timestamps of historical node infections. The target network is customarily assumed as an one-aspect diffusion network, with homogeneous influence relationships. Nonetheless, tracing node infection timestamps is often infeasible due to high cost, and the type of influence relationships may be heterogeneous because of the diversity of propagation media. In this work, we study how to infer a multi-aspect diffusion network with heterogeneous influence relationships, using only node infection statuses that are more readily accessible in practice. Equipped with a probabilistic generative model, we iteratively conduct a posteriori, quantitative analysis on historical diffusion results of the network, and infer the structure and strengths of homogeneous influence relationships in each aspect. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.

References

[1]

B. Abrahao, F. Chierichetti, and R. Kleinberg. 2013. Trace Complexity of Network Inference. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013). 491–499.

Digital Library

[2]

K. Amin, H. Heidari, and M. Kearns. 2014. Learning from Contagion(Without Timestamps). In Proceedings of the 31st International Conference on Machine Learning (ICML 2014). 1845–1853.

[3]

W. Chen, X. Sun, J. Zhang, and Z. Zhang. 2021. Network inference and influence maximization from samples. In Proceedings of the 38th International Conference on Machine Learning (ICML 2021). 1707–1716.

[4]

H. Daneshmand, M. Gomez-Rodriguez, L. Song, and B. Schölkopf. 2014. Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. In Proceedings of the 31st International Conference on Machine Learning (ICML 2014). 793–801.

[5]

N. Du, L. Song, A. Smola, and M. Yuan. 2012. Learning Networks of Heterogeneous Influence. In Advances in Neural Information Processing Systems 25 (NIPS 2012). 2780–2788.

[6]

T. Gan, K. Han, H. Huang, S. Ying, Y. Gao, and Z. Li. 2021. Diffusion Network Inference from Partial Observations. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021). 7493–7500.

[7]

M. Gomez-Rodriguez, D. Balduzzi, and B. Schölkopf. 2011. Uncovering the Temporal Dynamics of Diffusion Networks. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011). 561–568.

[8]

M. Gomez-Rodriguez, J. Leskovec, and A. Krause. 2010. Inferring Networks of Diffusion and Influence. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). 1019–1028.

Digital Library

[9]

M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. 2013. Modeling Information Propagation with Survival Theory. In Proceedings of the 30th International Conference on Machine Learning (ICML 2013). 666–674.

[10]

M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. 2013. Structure and Dynamics of Information Pathways in Online Media. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013). 23–32.

Digital Library

[11]

M. Gomez-Rodriguez and B. Schölkopf. 2012. Submodular Inference of Diffusion Networks from Multiple Trees. In Proceedings of the 29th International Conference on Machine Learning (ICML 2012). 489–496.

[12]

K. Han, Y. Tian, Y. Zhang, L. Han, H. Huang, and Y. Gao. 2020. Statistical Estimation of Diffusion Network Topologies. In Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE 2020). 625–636.

[13]

X. He, T. Rekatsinas, J. Foulds, L. Getoor, and Y. Liu. 2015. HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). 871–880.

[14]

X. He, K. Xu, D. Kempe, and Y. Liu. 2016. Learning Influence Functions from Incomplete Observations. In Advances in Neural Information Processing Systems 29 (NIPS 2016). 2065–2073.

[15]

H. Huang, K. Han, B. Xu, and T. Gan. 2022. Reconstructing diffusion networks from incomplete data. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022). 3085–3091.

[16]

H. Huang, Q. Yan, L. Chen, Y. Gao, and C. S. Jensen. 2021. Statistical Inference of Diffusion Networks. IEEE Transactions on Knowledge and Data Engineering 33, 2 (2021), 742–753.

Digital Library

[17]

H. Huang, Q. Yan, T. Gan, D. Niu, W. Lu, and Y. Gao. 2019. Learning Diffusions without Timestamps. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019). 582–589.

[18]

D. Kalimeris, Y. Singer, K. Subbian, and U. Weinsberg. 2018. Learning Diffusion using Hyperparameters. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). 2420–2428.

[19]

A. Lancichinetti, S. Fortunato, and F. Radicchi. 2008. Benchmark Graphs for Testing Community Detection Algorithms. Physical Review E 78, 4 (2008).

[20]

J. Leskovec, L. A. Adamic, and B. A. Huberman. 2007. The Dynamics of Viral Marketing. ACM Transactions on the Web 1, 1 (2007), 5.

Digital Library

[21]

H. Li, C. Xia, T. Wang, S. Wen, C. Chen, and X. Yang. 2021. Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques. ACM Computing Surveys (CSUR) 55, 1 (2021), 1 – 51.

Digital Library

[22]

A. Lokhov. 2016. Reconstructing Parameters of Spreading Models from Partial Observations. In Advances in Neural Information Processing Systems 29 (NIPS 2016). 3467–3475.

[23]

L. Ma, H. Huang, Q. He, K. Chiew, and Z. Liu. 2014. Toward Seed-Insensitive Solutions to Local Community Detection. Journal of Intelligent Information Systems 43, 1 (2014), 183–203.

Digital Library

[24]

L. Ma, H. Huang, Q. He, K. Chiew, J. Wu, and Y. Che. 2013. GMAC: A Seed-Insensitive Approach to Local Community Detection. In Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2013). 297–308.

Digital Library

[25]

S. Myers and J. Leskovec. 2010. On the Convexity of Latent Social Network Inference. In Advances in Neural Information Processing Systems 23 (NIPS 2010). 1741–1749.

[26]

H. Narasimhan, D. C. Parkes, and Y. Singer. 2015. Learnability of Influence in Networks. In Advances in Neural Information Processing Systems 28 (NIPS 2015). 3186–3194.

[27]

P. Netrapalli and S. Sanghavi. 2012. Learning the Graph of Epidemic Cascades. In Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS 2012). 211–222.

Digital Library

[28]

Z. Peng, T. Wang, W. Lu, H. Huang, X. Du, F. Zhao, and A. K. Tung. 2018. Mining Frequent Subgraphs from Tremendous Amount of Small Graphs Using MapReduce. Knowledge and Information Systems 56, 3 (2018), 663–690.

Digital Library

[29]

J. Pouget-Abadie and T. Horel. 2015. Inferring Graphs from Cascades: A Sparse Recovery Framework. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). 977–986.

[30]

Y. Rong, Q. Zhu, and H. Cheng. 2016. A Model-Free Approach to Infer the Diffusion Network from Event Cascade. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016). 1653–1662.

[31]

E. Sefer and C. Kingsford. 2015. Convex Risk Minimization to Infer Networks from Probabilistic Diffusion Data at Multiple Scales. In Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE 2015). 663–674.

[32]

J. Wallinga and P. Teunis. 2004. Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures. American Journal of Epidemiology 160, 6 (2004), 509–516.

[33]

S. Wang, X. Hu, P. Yu, and Z. Li. 2014. MMRate: Inferring Multi-aspect Diffusion Networks with Multi-pattern Cascades. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014). 1246–1255.

Digital Library

[34]

T. Wang, H. Huang, W. Lu, Z. Peng, and X. Du. 2018. Efficient and Scalable Mining of Frequent Subgraphs Using Distributed Graph Processing Systems. In Proceedings of the 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018). 891–907.

Digital Library

[35]

M. Wilinski and A. Lokhov. 2021. Prediction-centric learning of independent cascade dynamics from partial observations. In Proceedings of the 38th International Conference on Machine Learning (ICML 2021). 11182–11192.

[36]

Q. Yan, H. Huang, Y. Gao, W. Lu, and Q. He. 2017. Group-Level Influence Maximization with Budget Constraint. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017). 625–641.

Cited By

Huang HYan QHan KGan TJiang JXu QYang CWooldridge MDy JNatarajan S(2024)Learning diffusions under uncertaintyProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i18.30026(20430-20437)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i18.30026
Wang YHou DGao CLi XWang ZSerra ESpezzano F(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
https://dl.acm.org/doi/10.1145/3627673.3679798
Zhao YYao XHuang H(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
https://doi.org/10.1007/s10489-024-05620-w
Show More Cited By

Index Terms

Multi-aspect Diffusion Network Inference
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning in probabilistic graphical models
        Maximum likelihood modeling
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic inference problems
      1. Computing most probable explanation

Recommendations

Motif-Aware Diffusion Network Inference
Advances in Knowledge Discovery and Data Mining
Abstract
Characterizing and understanding information diffusion over social networks play an important role in various real-world applications. In many scenarios, however, only the states of nodes can be observed while the underlying diffusion networks are ...
Inference in multi-agent causal models

In this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform ...
Variational probabilistic inference and the QMR-DT network

We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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].

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '23

Sponsor:

SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
409
Total Downloads

Downloads (Last 12 months)80
Downloads (Last 6 weeks)7

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Huang HYan QHan KGan TJiang JXu QYang CWooldridge MDy JNatarajan S(2024)Learning diffusions under uncertaintyProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i18.30026(20430-20437)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i18.30026
Wang YHou DGao CLi XWang ZSerra ESpezzano F(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
https://dl.acm.org/doi/10.1145/3627673.3679798
Zhao YYao XHuang H(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
https://doi.org/10.1007/s10489-024-05620-w
Yan QYang YYin KGan THuang H(2024)Diffusion pattern miningKnowledge and Information Systems10.1007/s10115-024-02254-967:2(1101-1129)Online publication date: 12-Oct-2024
https://doi.org/10.1007/s10115-024-02254-9
Guo J(2024)Online Influence Maximization: Concept and AlgorithmHandbook of Combinatorial Optimization10.1007/978-1-4614-6624-6_100-1(1-29)Online publication date: 19-Oct-2024
https://doi.org/10.1007/978-1-4614-6624-6_100-1

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten