Network Embedding Methods For Large Networks In Political Science
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Network Embedding Methods for Large Networks in Political Science
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.
Social Network Analysis
Author: Song Yang
language: en
Publisher: SAGE Publications, Incorporated
Release Date: 2016-12-02
Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.
Network Embedding
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.