Correlation Between Clustering and Degree in Affiliation Networks

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

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Inference in a probit model for affiliation networks

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1873381 ◽

2021 ◽

pp. 1-22

Author(s):

Qian Wang ◽

Chen Zhao ◽

Jing Luo

Keyword(s):

Probit Model ◽

Affiliation Networks

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Organizational Closeness Centralities of Workflow-Supported Performer-to-Activity Affiliation Networks

IEEE Access ◽

10.1109/access.2021.3065925 ◽

2021 ◽

Vol 9 ◽

pp. 48555-48582

Author(s):

Hyun Ahn ◽

Kwanghoon Pio Kim

Keyword(s):

Affiliation Networks

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Exploratory social network analysis of affiliation networks of Indian listed companies

Social Networks ◽

10.1016/j.socnet.2015.03.008 ◽

2015 ◽

Vol 43 ◽

pp. 113-120 ◽

Cited By ~ 21

Author(s):

C. Prem Sankar ◽

K. Asokan ◽

K. Satheesh Kumar

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Listed Companies ◽

Affiliation Networks

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Understanding actor loyalty to event-based groups in affiliation networks

Social Network Analysis and Mining ◽

10.1007/s13278-010-0005-5 ◽

2010 ◽

Vol 1 (2) ◽

pp. 115-126 ◽

Cited By ~ 7

Author(s):

Hossam Sharara ◽

Lisa Singh ◽

Lise Getoor ◽

Janet Mann

Keyword(s):

Affiliation Networks ◽

Event Based

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Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses

Entropy ◽

10.3390/e21030277 ◽

2019 ◽

Vol 21 (3) ◽

pp. 277

Author(s):

Wilhelm Rödder ◽

Andreas Dellnitz ◽

Friedhelm Kulmann ◽

Sebastian Litzinger ◽

Elmar Reucher

Keyword(s):

Social Networks ◽

Maximum Entropy ◽

Point Of View ◽

Theoretical Point ◽

Generic Structure ◽

New Approach ◽

Affiliation Networks ◽

Principle Of Maximum Entropy ◽

Affiliation Network ◽

Principle Of Maximum

A special type of social networks is the so-called affiliation network, consisting of two modes of vertices: actors and events. Up to now, in the undirected case, the closeness of actors in such networks has been measured by their jointly-attended events. Indirect contacts and attenuated and directed links are of minor interest in affiliation networks. These flaws make a veritable estimation of, e.g., possible message transfers amongst actors questionable. In this contribution, first, we discuss these matters from a graph-theoretical point of view. Second, so as to avoid the identified weaknesses, we propose an up-and-coming entropy-based approach for modeling such networks in their generic structure, replacing directed (attenuated) links by conditionals: if-then. In this framework, the contribution of actors and events to a reliable message transfer from one actor to another—even via intermediaries—is then calculated applying the principle of maximum entropy. The usefulness of this new approach is demonstrated by the analysis of an affiliation network called “corporate directors”.

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Triadic analysis of affiliation networks

Network Science ◽

10.1017/nws.2015.38 ◽

2015 ◽

Vol 3 (4) ◽

pp. 480-508 ◽

Cited By ~ 1

Author(s):

JASON CORY BRUNSON

Keyword(s):

Social Networks ◽

Clustering Coefficient ◽

Affiliation Networks ◽

Instrumental Assessment ◽

Triadic Closure ◽

The One

AbstractTriadic closure has been conceptualized and measured in a variety of ways, most famously the clustering coefficient. Existing extensions to affiliation networks, however, are sensitive to repeat group attendance, which does not reflect common interpersonal interpretations of triadic closure. This paper proposes a measure of triadic closure in affiliation networks designed to control for this factor, which manifests in bipartite models as biclique proliferation. To avoid arbitrariness, the paper introduces a triadic framework for affiliation networks, within which a range of measures can be defined; it then presents a set of basic axioms that suffice to narrow this range to the one measure. An instrumental assessment compares the proposed and two existing measures for reliability, validity, redundancy, and practicality. All three measures then take part in an investigation of three empirical social networks, which illustrates their differences.

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