A comparison of email networks and off-line social networks: A study of a medium-sized bank

Johnson, Rebeka; Kovács, Balázs; Vicsek, András

doi:10.1016/j.socnet.2012.02.004

Cited by 39 publications

(20 citation statements)

References 22 publications

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“…[5] Introduced a measure for evaluating the goodness of partitioning known as modularity which states that it is better to investigate community structure by provisioning nested hierarchy rather single community partitioning. The most prevailing and predominant technique which sociologists use in their analysis of social network and community identification is hierarchical clustering [7] [8].…”

Section: IImentioning

confidence: 99%

Email Analysis for Community Detection - Multi-User Perspective

Karthika¹,

Saranya²

2017

IJAERS

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Section: IImentioning

confidence: 99%

Email Analysis for Community Detection - Multi-User Perspective

Karthika¹,

Saranya²

2017

IJAERS

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“…As can be seen from Table , the in degree and the out degree of most of the elements, as well as the connectivity composed of both are much higher, that means there is mutual influence between the elements, so it is not reasonable to identify key elements according to them [16]. To identify the key elements, we need to calculate closeness centrality of adjacency matrix.…”

Section: B Key Elements Identification In Cooperative Technological mentioning

confidence: 99%

Key Element Identification in Cooperative Technological Innovation Risk on Social Network Analysis

2014

2014 Seventh International Joint Conference on Computational Sciences and Optimization

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Cooperative technological innovation, with various hidden risks, often leads to technological innovation aborted. In order to deal with risks, this paper defines cooperative technological innovation risk element networks from the viewpoint of system theory. Based on the network characteristics, the paper introduces S ocial Network Analysis (SNA) to build a key element recognition model through the three dimensions of factor analysis, relations analysis, matrix analysis from cooperative technological innovation risk elements. Finally, the paper tests the model based on an example and identifies eight key elements of cooperative technological innovation risks. Keywords-offshore equipment manufactu ring industry cooperative technological innovation risk; social network analysis; key element identification

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“…In order to determine a relation, however, we must obviously decide what constitutes a relationship. A common approach in both simulation (e.g., Hirshman et al, 2011) and empirical studies (e.g., Johnson, Kovács, & Vicsek, 2012) is to use a cutoff of interaction counts, where only agents having more than N interactions between them are considered to have a social relation. In the present work, we choose N ¼ 2 (the same used by Hirshman et al, 2011), providing us with social networks having a mean density of around .016 (around 16 ties per agent).…”

Section: Outcome Metricmentioning

confidence: 99%

On the Coevolution of Stereotype, Culture, and Social Relationships

Joseph

Morgan

Martin

et al. 2013

Social Science Computer Review

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The theory of constructuralism describes how shared knowledge, representative of cultural forms, develops between individuals through social interaction. Constructuralism argues that through interaction and individual learning, the social network (who interacts with whom) and the knowledge network (who knows what) coevolve. In the present work, we extend the theory of constructuralism and implement this extension in an agent-based model (ABM). Our work focuses on the theory's inability to describe how people form and utilize stereotypes of higher order social structures, in particular observable social groups and society as a whole. In our ABM, we formalize this theoretical extension by creating agents that construct, adapt, and utilize social stereotypes of individuals, social groups, and society. We then use this model to carry out a virtual experiment that explores how ethnocentric stereotypes and the underlying distribution of culture in an artificial society interact to produce varying levels of social relationships across social groups. In general, we find that neither stereotypes nor the form of underlying cultural structures alone are sufficient to explain the extent of social relationships across social groups. Rather, we provide evidence that shared culture, social relations, and group stereotypes all intermingle to produce macrosocial structure.

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A comparison of email networks and off-line social networks: A study of a medium-sized bank

Cited by 39 publications

References 22 publications

Email Analysis for Community Detection - Multi-User Perspective

Email Analysis for Community Detection - Multi-User Perspective

Key Element Identification in Cooperative Technological Innovation Risk on Social Network Analysis

On the Coevolution of Stereotype, Culture, and Social Relationships

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