Social Network Analysis in Enterprise

Social network analysis (SNA) has been a research focus in multiple disciplines for decades, including sociology, healthcare, business management, etc. Traditional SNA researches concern more human and social science aspects—trying to undermine the real relationship of people and the impacts of these relationships. While online social networks have become popular in recent years, social media analysis, especially from the viewpoint of computer scientists, is usually limited to the aspects of people's behavior on specific websites and thus are considered not necessarily related to the day-to-day people's behavior and relationships. We conduct research to bridge the gap between social scientists and computer scientists by exploring the multifacet existing social networks in organizations that provide better insights on how people interact with each other in their professional life. We describe a comprehensive study on the challenges and solutions of mining and analyzing existing social networks in enterprise. Several aspects are considered, including system issues; privacy laws; the economic value of social networks; people's behavior modeling including channel, culture, and social inference; social network visualization in large-scale organization; and graph query and mining. The study is based on an SNA tool (SmallBlue) that was designed to overcome practical challenges and is based on the data collected in a global organization of more than 400 000 employees in more than 100 countries.

[1]  Christopher Chute,et al.  The Diverse and Exploding Digital Universe , 2011 .

[2]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[3]  Philippe Castagliola,et al.  A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations , 2004, IEEE Symposium on Information Visualization.

[4]  R. Burt.  Structural Holes and Good Ideas1 , 2004, American Journal of Sociology.

[5]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[7]  Jimeng Sun,et al.  HiMap: Adaptive visualization of large-scale online social networks , 2009, 2009 IEEE Pacific Visualization Symposium.

[8]  Robin I. M. Dunbar.  Neocortex size as a constraint on group size in primates , 1992 .

[9]  Jingrui He,et al.  Diversified ranking on large graphs: an optimization viewpoint , 2011, KDD.

[10]  Michael E. W. Varnum,et al.  The Origin of Cultural Differences in Cognition , 2010 .

[11]  P. V. Marsden,et al.  NETWORK DATA AND MEASUREMENT , 1990 .

[12]  Kozo Sugiyama,et al.  Layout Adjustment and the Mental Map , 1995, J. Vis. Lang. Comput..

[13]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[14]  V. Mirrokni,et al.  Tight approximation algorithms for maximum general assignment problems , 2006, SODA 2006.

[15]  Ana Paula Appel,et al.  Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations , 2010, SDM.

[16]  Mark S. Ackerman,et al.  Culture Matters: A Survey Study of Social Q&A Behavior , 2011, ICWSM.

[17]  Jimeng Sun,et al.  Neighborhood formation and anomaly detection in bipartite graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[18]  Ching-Yung Lin,et al.  Recommendation in the end-to-end encrypted domain , 2011, CIKM '11.

[19]  Ching-Yung Lin,et al.  Improving user interest inference from social neighbors , 2011, CIKM '11.

[20]  Ching-Yung Lin,et al.  On the quality of inferring interests from social neighbors , 2010, KDD.

[21]  Vahab S. Mirrokni,et al.  Tight approximation algorithms for maximum general assignment problems , 2006, SODA '06.

[22]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[23]  Lynn Wu,et al.  Social Network Effects on Performance and Layoffs: Evidence from the Adoption of a Social Networking Tool , 2011, ICIS.

[24]  Mike Freeman,et al.  The Visible Employee , 2007 .

[25]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[26]  A. Zaheer,et al.  Bridging ties: a source of firm heterogeneity in competitive capabilities , 1999 .

[27]  Ching-yung Lin.  Information Flow Prediction by Modeling Dynamic Probabilistic Social Network , 2006 .

[28]  Christos Faloutsos,et al.  PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[29]  Jimeng Sun,et al.  GBASE: a scalable and general graph management system , 2011, KDD.

[30]  F. Galton,et al.  On the Probability of the Extinction of Families , 1875 .

[31]  Kate Ehrlich,et al.  SmallBlue: People Mining for Expertise Search , 2008, IEEE MultiMedia.

[32]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[33]  Benjamin N. Waber,et al.  Mining Face-to-Face Interaction Networks Using Sociometric Badges: Evidence Predicting Productivity in IT Configuration , 2008 .

[34]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[35]  Hanghang Tong,et al.  Information spreading in context , 2011, WWW.

[36]  David R. Millen,et al.  Dogear: Social bookmarking in the enterprise , 2006, CHI.

[37]  Christos Faloutsos,et al.  oddball: Spotting Anomalies in Weighted Graphs , 2010, PAKDD.

[38]  David R. Millen,et al.  Social Bookmarking in the Enterprise , 2005, ACM Queue.

[39]  Matthew O Jackson,et al.  Using selection bias to explain the observed structure of Internet diffusions , 2010, Proceedings of the National Academy of Sciences.

[40]  Jimeng Sun,et al.  SmallBlue: Social Network Analysis for Expertise Search and Collective Intelligence , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[41]  Kate Ehrlich,et al.  Searching for experts in the enterprise: combining text and social network analysis , 2007, GROUP.

[42]  Mark S. Ackerman,et al.  Collaborating Globally: Culture and Organizational Computer-Mediated Communications , 2011, ICIS.

[43]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[44]  Ching-Yung Lin,et al.  ExpertiseNet: Relational and Evolutionary Expert Modeling , 2005, User Modeling.

[45]  Filip Radlinski,et al.  Redundancy, diversity and interdependent document relevance , 2009, SIGF.

[46]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[47]  Ezra W. Zuckerman,et al.  Networks, Diversity, and Productivity: The Social Capital of Corporate R&D Teams , 2001 .

[48]  Hong Zhou,et al.  Geometry-Based Edge Clustering for Graph Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[49]  Hanghang Tong,et al.  Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection , 2011, SDM.