Network Embedding Methods for Large Networks in Political Science

25 Pages Posted: 14 Nov 2021

See all articles by Megan A. Brown

Megan A. Brown

Center for Social Media and Politics - NYU

Zhanna Terechshenko

New York University (NYU)

Rachel Connolly

affiliation not provided to SSRN

Angela Lai

New York University (NYU)

Charlotte Ji

New York University (NYU) - Center for Data Science

Jonathan Nagler

NYU - Wilf Family Department of Politics

Joshua A. Tucker

New York University (NYU)

Richard Bonneau

New York University (NYU) - New York University

Date Written: November 12, 2021

Abstract

Social networks play an important role in many political science studies. With the rise of social media, these networks have grown in both size and complexity. Analysis of these large networks requires generation of feature representations that can be used in machine learning models. One way to generate these feature representations is to use network embedding methods for learning low-dimensional feature representations of nodes and edges in a network. While there is some literature comparing the advantages and shortcomings of these models, to our knowledge, there has not been any analysis on the applicability of network embedding models to classification tasks in political science. In this paper, we compare the performance of five prominent network embedding methods on prediction of ideology of Twitter users and ideology of Internet domains. We find that LINE provides the best feature representation across all 4 datasets that we use, resulting in the highest performance accuracy. Finally, we provide the guidelines for researchers on the use of these models for their own research.

Keywords: Social networks, network embedding, social media, methodology

Suggested Citation

Brown, Megan and Terechshenko, Zhanna and Connolly, Rachel and Lai, Angela and Ji, Charlotte and Nagler, Jonathan and Tucker, Joshua Aaron and Bonneau, Richard, Network Embedding Methods for Large Networks in Political Science (November 12, 2021). Available at SSRN: https://ssrn.com/abstract=3962536 or http://dx.doi.org/10.2139/ssrn.3962536

Megan Brown (Contact Author)

Center for Social Media and Politics - NYU ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Zhanna Terechshenko

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Rachel Connolly

affiliation not provided to SSRN

Angela Lai

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Charlotte Ji

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Jonathan Nagler

NYU - Wilf Family Department of Politics ( email )

Dept of Politics - 2nd floor
19 W. 4th Street
New York, NY 10012
United States

Joshua Aaron Tucker

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Richard Bonneau

New York University (NYU) - New York University ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

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