We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

/pdf/business-intelligence-and-analytics-from-big-data-to-big-3gk8u8as27.pdf

Business intelligence and analytics: from big data to big impact

Trust: The Social Virtues and the Creation of Prosperity

Social Network Analysis

https://ranger.uta.edu/~chqding/cse6319/classPapers/ExponentialRandomGraph.pdf

An introduction to exponential random graph (p * ) models for social networks

The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p∗ models. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basicpr obability models. Recently, Markov chain Monte Carlo (MCMC) algorithms have been developed that produce approximate maximum likelihood estimators. Applying these models in their traditional specification to observed network data often has led to problems, however, which can be traced back to the fact that important parts of the parameter space correspond to nearly degenerate distributions, which may lead to convergence problems of estimation algorithms, and a poor fit to empirical data. This paper proposes new specifications of exponential random graph models. These specifications represent structural properties such as transitivity and heterogeneity of degrees by more complicated graph statistics than the traditional star and triangle counts. Three kinds of statistics are proposed: geometrically weighted degree distributions, alternating k-triangles, and alternating independent two-paths. Examples are presented both of modeling graphs and digraphs, in which the new specifications lead to much better results than the earlier existing specifications of the ERGM. It is concluded that the new specifications increase the range and applicability of the ERGM as a tool for the statistical analysis of social networks.

/pdf/new-specifications-for-exponential-random-graph-models-48maumzi64.pdf

New specifications for exponential random graph models

This is the beginning of Network Science. The journal has been created because network science is exploding. As is typical for a field in formation, the discussions about its scope, contents, and foundations are intense. On these first few pages of the first issue of our new journal, we would like to share our own vision of the emerging science of networks.

/pdf/what-is-network-science-34c8vflllq.pdf

What is network science

http://www.stats.ox.ac.uk/~snijders/RobinsSnijdersWangHandcockPattison.pdf

Recent developments in exponential random graph (p*) models for social networks

Introduction Dean Lusher, Johan Koskinen and Garry Robins 1. What are exponential random graph models Garry Robins and Dean Lusher 2. The formation of social network structure Dean Lusher and Garry Robins 3. A simplified account of ERGM as a statistical model Garry Robins and Dean Lusher 4. An example of ERGM analysis Dean Lusher and Garry Robins 5. Exponential random graph model fundamentals Johan Koskinene and Galina Daraganova 6. Dependence graphs and sufficient statistics Johan Koskinen and Galina Daraganova 7. Social selection, dyadic covariates and geospatial effects Garry Robins and Galina Daraganova 8. Autologistic actor attribute models Galina Daraganova and Garry Robins 9. ERGM extensions: models for multiple networks and bipartite networks Peng Wang 10. Longitudinal models Tom Snijders and Johan Koskinen 11. Simulation, estimation and goodness of fit Johan Koskinen and Tom Snijders 12. Illustrations: simulation, estimation and goodness of fit Garry Robins and Dean Lusher 13. Personal attitudes, perceived attitudes and social structures: a social selection model Dean Lusher and Garry Robins 14. How to close a hole: exploring alternative closure mechanisms in inter-organizational networks Alessandro Lomi and Francesca Pallotti 15. Interdependencies between working relations: multivariate ERGMs for advice and satisfaction Yu Zhao and Olaf Rank 16. Brain, brawn or optimism? The structure and correlates of emergent military leadership Yuval Kalish and Gil Luria 17. An ALAAM analysis of unemployment: the dual importance of who you know and where you live Galina Daraganova and Philippa Pattison 18. Longitudinal changes in face-to-face and text message-mediated friendship networks Tasuku Igarashi 19. The differential impact of directors' social and financial capital on corporate interlock formation Nicholas Harrigan and Matthew Bond 20. Comparing networks: a structural correspondence between behavioural and recall networks Eric Quintane 21. Modelling social networks: next steps Philippa Pattison and Tom Snijders.

Garry Robins

Papers

An introduction to exponential random graph (p * ) models for social networks

New specifications for exponential random graph models

What is network science

Recent developments in exponential random graph (p*) models for social networks

Exponential Random Graph Models for Social Networks: Theory, Methods and Applications