research-article

Theory and use case of game-theoretic lexical link analysis

Authors:

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 717 - 720

https://doi.org/10.1145/3341161.3343706

Published: 15 January 2020 Publication History

Get Access

Abstract

We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.

References

[1]

Center for Computational Analysis of Social and Organizational Systems (CASOS), AutoMap: extract, analyze and represent relational data from texts, 2009. Retrieved from http://www.casos.cs.cmu.edu.

Google Scholar

[2]

Girvan, M., and Newman, M. E. J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, USA, 99(12), 78217826, 2002.

Crossref

Google Scholar

[3]

Freeman, L.C. Centrality in social networks I: conceptual clarification. Social Networks, 1: 215--239, 1979.

Crossref

Google Scholar

[4]

Brin, S. and Page, L. The Anatomy of a large-scale hypertextual web search Engine, Computer Networks and ISDN Systems, 30:107-117, 1998.

Digital Library

Google Scholar

[5]

Newman, M. E. J. Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, vol. 74, no. 3, 2006.

Crossref

Google Scholar

[6]

Miller, G. A. WordNet: a lexical database for English.,Communications of the ACM, 38(11), 1995.

Google Scholar

[7]

Blei, D., Ng, A. and Jordan, M., Latent Dirichlet allocation, Journal of Machine Learning Research, 3:993-1022, 2003. Retrieved from http://jmlr.csail.mit.edu/papers/volume3/blei03a/blei03a.pdf.

Digital Library

Google Scholar

[8]

US patent 8,903,756. System and method for knowledge pattern search from networked agents, 2014. Retrieved from https://www.google.com/patents/US8903756

Google Scholar

[9]

Nash, J. (1950). Equilibrium points in n-person games. Proceedings of the National Academy of Sciences 36(1):48-49.

Crossref

Google Scholar

[10]

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B.; Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems, 26722680. Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y. (2017). Generalization and equilibrium in generative adversarial nets (GANs). arXiv preprint arXiv:1703.00573.

Google Scholar

Cited By

View all

Zhao YZhou C(2020)Link Analysis to Discover Insights from Structured and Unstructured Data on COVID-19Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3415990(1-8)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3415990

Recommendations

A game-theoretic lexical link analysis for discovering high-value information from big data
ASONAM '18: Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

We demonstrate a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover high-value information from big data. In this paper, high-value information refers to the information that has the potential to grow its ...
Multilayer value metrics using lexical link analysis and game theory for discovering innovation from big data and crowd-sourcing
ASONAM '18: Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover different layers of semantic network that contribute to innovative ideas from big data. The LLA is an unsupervised machine learning ...
Quantum Theoretic Values of Collaborative and Self-organizing Agents
ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

When multiple agents collaborate to perform distributed operations, they can be modeled as cooperative games. Considering a network of agents work together and they can only communicate in a limited way (e.g., only to neighbor peers), the goal is to ...

Comments

Information & Contributors

Information

Published In

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

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 ACM 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]

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ASONAM '19

Sponsor:

SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

Acceptance Rates

ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
65
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhao YZhou C(2020)Link Analysis to Discover Insights from Structured and Unstructured Data on COVID-19Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3415990(1-8)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3415990

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

A game-theoretic lexical link analysis for discovering high-value information from big data

Multilayer value metrics using lexical link analysis and game theory for discovering innovation from big data and crowd-sourcing

Quantum Theoretic Values of Collaborative and Self-organizing Agents

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations