E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations

Abstract

Abstract. We describe a methodology for the automatic identification of communities of practice from email logs within an organization. We use a betweenness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.

Models of social networks based on social distance attachment

“…Analogously, an alternative explanation for collaboration networks [8] does not apply in the more general context of acquaintance networks Community structure. Social networks possess a complex community structure [12,21,22], in which individuals typically belong to groups or communities, with a high density of internal connections and loosely connected among them, that on their turn belong to groups of groups and so on, giving raise to a hierarchy of nested social communities of practice showing in some cases a self-similar structure [12]. Several authors [8,12,22] have advocated this last property, the presence of a community structure, as the very distinguishing feature of social networks, responsible for the rest of the properties that differentiate those from nonsocial networks.…”

Asymmetric Multidimensional Scaling of Brand Switching Among Margarine Brands

“…Okada (2008) introduced more than one set of centralities for the symmetric social network which widened the concept of the centrality. While the relationships among objects were assumed to be symmetric this did not always turn out to hold true; the relationship from objects j to k is not always equal to the relationship from objects k to j (Barnet & Rice, 1985;Bonacich & Lloyd, 2001;Tyler, Wilkinson, & Huberman, 2003;Wasserman & Faust, 1994, pp.124-125, 510-511). The centrality of the asymmetric social network has been introduced by several researchers (Bonacich & Lloyd, 2001;Okada, 2010;Write & Evitts, 1961).…”

Integrating social network data into GISystems

“…Second, telecommunications or Information and Communications Technology (ICT) data also evidence social relationships as sourced from e-mails (e.g., Tyler et al 2005), SMS, websites, or instant message (IM) services (e.g., Leskovec and Horvitz 2008); geotagged photo uploads (e.g., Crandall et al 2010) online check-in sites (e.g., Long et al 2012), postal mail (e.g., Milgram 1967), point-to-point landline or mobile phone calls (e.g., Eagle et al 2010), or IP address hits to certain geolocated websites.…”

“…Third, stated relationships are sourced from institutional records, surveys, online groups, self-report, interviews, and recorded data from communities, families, and institutions, such as businesses (e.g., Tyler et al 2005), schools (e.g., Kirke 1996, Moody 2001, clubs (e.g., Zachary 1977), political or public figure networks (e.g., Andris et al 2015b), or online friendships (e.g., Crandall et al 2010).…”