MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Jihoon Ko; Kyuhan Lee; Kijung Shin; Noseong Park

doi:10.1109/ASONAM49781.2020.9381460

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park

Graduate School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Influence maximization (IM) is one of the most important problems in social network analysis. Its objective is to find a given number of seed nodes that maximize the spread of information through a social network. Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeat Monte Carlo (MC) simulations over and over to reliably estimate the influence (i.e., the number of infected nodes) of a seed set. In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training. To the best of our knowledge, MONSTOR is the first inductive method for this purpose. MONSTOR can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Moreover, IM algorithms equipped with MONSTOR are more accurate than state-of-the-art competitors in 63% of IM use cases.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	204-211
Number of pages	8
ISBN (Electronic)	9781728110561
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381460
Publication status	Published - 2020 Dec 7
Event	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 2020 Dec 7 → 2020 Dec 10

Publication series

Name	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/Territory	Netherlands
City	Virtual, Online
Period	20/12/7 → 20/12/10

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1C1C1008296), the Yonsei University Research Fund of 2020-22-0074, and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST) and No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). This research was results of a study on the “HPC Support” Project, supported by the ‘Ministry of Science and ICT’ and NIPA.

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management
Social Psychology
Communication

Access to Document

10.1109/ASONAM49781.2020.9381460

Cite this

Ko, J., Lee, K., Shin, K., & Park, N. (2020). MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 (pp. 204-211). [9381460] (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM49781.2020.9381460

Ko, Jihoon ; Lee, Kyuhan ; Shin, Kijung et al. / MONSTOR : An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 204-211 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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abstract = "Influence maximization (IM) is one of the most important problems in social network analysis. Its objective is to find a given number of seed nodes that maximize the spread of information through a social network. Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeat Monte Carlo (MC) simulations over and over to reliably estimate the influence (i.e., the number of infected nodes) of a seed set. In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training. To the best of our knowledge, MONSTOR is the first inductive method for this purpose. MONSTOR can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Moreover, IM algorithms equipped with MONSTOR are more accurate than state-of-the-art competitors in 63% of IM use cases.",

author = "Jihoon Ko and Kyuhan Lee and Kijung Shin and Noseong Park",

note = "Funding Information: ACKNOWLEDGEMENTS This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1C1C1008296), the Yonsei University Research Fund of 2020-22-0074, and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST) and No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). This research was results of a study on the “HPC Support” Project, supported by the {\textquoteleft}Ministry of Science and ICT{\textquoteright} and NIPA. Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 ; Conference date: 07-12-2020 Through 10-12-2020",

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Ko, J, Lee, K, Shin, K & Park, N 2020, MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020., 9381460, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 204-211, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Netherlands, 20/12/7. https://doi.org/10.1109/ASONAM49781.2020.9381460

MONSTOR : An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. / Ko, Jihoon; Lee, Kyuhan; Shin, Kijung et al.

Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 204-211 9381460 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: ACKNOWLEDGEMENTS This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1C1C1008296), the Yonsei University Research Fund of 2020-22-0074, and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST) and No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). This research was results of a study on the “HPC Support” Project, supported by the ‘Ministry of Science and ICT’ and NIPA. Publisher Copyright: © 2020 IEEE.

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Ko J, Lee K, Shin K, Park N. MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 204-211. 9381460. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381460

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

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