Quantifying triadic closure in multi-edge social networks

Social network structure has often been attributed to two network evolution mechanisms—triadic closure and choice homophily—which are commonly considered independently or with static models. However, empirical studies suggest that their dynamic interplay generates the observed homophily of real-world social networks. By combining these mechanisms in a dynamic model, we confirm the longheld hypothesis that choice homophily and triadic closure cause induced homophily. We estimate how much observed homophily in friendship and communication networks is amplified due to triadic closure. We find that cumulative effects of homophily amplification can also lead to the widely documented core-periphery structure of networks, and to memory of homophilic constraints (equivalent to hysteresis in physics). The model shows that even small individual bias may prompt network-level changes such as segregation or core group dominance. Our results highlight that individual-level mechanisms should not be analyzed separately without considering the dynamics of society as a whole.

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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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Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

Scientific Programming ◽

10.1155/2021/5553566 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Nauman Ali Khan ◽

Wuyang Zhou ◽

Mudassar Ali Khan ◽

Ahmad Almogren ◽

Ikram Ud Din

Keyword(s):

Social Networks ◽

Social Network ◽

Research Direction ◽

Social Actors ◽

The Social ◽

Depth Analysis ◽

Location Based Social Network ◽

Triadic Closure ◽

Location Based Social Networks ◽

The Given

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

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Probabilistic Relation between Triadic Closure and the Balance of Social Networks in Presence of Influence

International Journal of Cyber Behavior Psychology and Learning ◽

10.4018/ijcbpl.2015100104 ◽

2015 ◽

Vol 5 (4) ◽

pp. 53-61

Author(s):

Rahul Saha ◽

G. Geetha ◽

Gulshan Kumar

Keyword(s):

Social Networks ◽

Social Networking ◽

Positive Relation ◽

Major Research ◽

Friendly Relation ◽

Global View ◽

The Social ◽

Triadic Closure ◽

Probabilistic Relation ◽

Negative Relationships

Data analysis in social networking is a major research concern in todays' environment in the field of interpretation models of data for any network. Social networking includes only two types of relationships: firstly, a friendly relation with whom one is having a link that can be considered as a positive relation and secondly, a relationship with which one is not connected or so called one's enemies labelled as negative relationships. Balanced theorem of social networking claims that all the nodes in the social network can be divided into two sets: a friendship set and an enemy set and provides the global view of relationships. In this paper, the authors have shown a probabilistic model to show that the global view of social links does not only depend on negative and positive relations to be distinguished, but it also depends on influences parameters.

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