Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

Multiplex Communities and the Emergence of International Conflict

Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.Comment: arXiv admin note: text overlap with arXiv:1802.0039

Using Course-Subject Co-Occurrence (CSCO) to Reveal the Structure of an Academic Discipline: A Framework to Evaluate Different Inputs of a Domain Map

This article proposes, exemplifies, and validates the use of course-subject co-occurrence (CSCO) data to generate topic maps of an academic discipline. A CSCO event is when two course-subjects are taught in the same academic year by the same teacher. 61,856 CSCO events were extracted from the 2010-11 directory of the American Association of Law Schools and used to visualize the structure of law school education in the United States. Different normalization, ordination (layout), and clustering algorithms were compared and the best performing algorithm of each type was used to generate the final map. Validation studies demonstrate that CSCO produces topic maps that are consistent with expert opinion and four other indicators of the topical similarity of law school course-subjects. This research is the first to use CSCO to produce a visualization of a domain. It is also the first to use an expanded, multi-part gold-standard to evaluate the validity of domain maps and the intermediate steps in their creation. It is suggested that the framework used herein may be adopted for other studies that compare different inputs of a domain map in order to empirically derive the best maps as measured against extrinsic sources of topical similarity (gold standards)