title: Inferring social status and rich club effects in enterprise communication networks.
abstract: Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels--voice call, short message, and email--to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a "rich club" maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.
authors.affiliation: Interdisciplinary Center for Network Science and Applications, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States of America.
authors.forename: Yuxiao
authors.initials: Y
authors.lastname: Dong
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, P. R. China.
authors.forename: Jie
authors.initials: J
authors.lastname: Tang
authors.affiliation: Interdisciplinary Center for Network Science and Applications, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States of America.
authors.forename: Nitesh V
authors.initials: NV
authors.lastname: Chawla
authors.affiliation: Google Inc, Mountain View, CA, United States of America.
authors.forename: Tiancheng
authors.initials: T
authors.lastname: Lou
authors.affiliation: Interdisciplinary Center for Network Science and Applications, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States of America.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China.
authors.forename: Bai
authors.initials: B
authors.lastname: Wang
mesh_terms: D003144:Communication Barriers; D034742:Electronic Mail / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D017149:Private Sector / Q000706:statistics & numerical data; D012923:Social Class; D060756:Social Networking; D013689:Telephone / Q000706:statistics & numerical data
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