David Krackhardt is Professor of Organizations at Heinz College and the Tepper School of Business, with courtesy appointments in the Department of Social and Decision Sciences (Dietrich College of Humanities and Social Sciences) and the Machine Learning Department (School of Computer Science), all at Carnegie Mellon University in the United States, and he also serves a Fellow of CEDEP, the European Centre for Executive Education, in France. He is notable for being the author of KrackPlot, a network visualization software designed for social network analysis which is widely used in academic research. He is also the founder of the Journal of Social Structure.
Krackhardt received a BS degree from the Massachusetts Institute of Technology and a PhD from the University of California, Irvine. He joined the Johnson School of Management at Cornell as an assistant professor in 1984. He moved to Harvard Business School as a Marvin Bower Fellow for a year, before joining Heinz College in 1991. He has held visiting faculty positions at the University of Chicago, INSEAD, and the University of Bocconi in Italy.
Krackhardt developed and showed the consequences of âÂÂcognitive social structuresâÂÂ, which capture how networks are perceived in a social environment. He created the E-I index, which describes the extent to which an organization is either silo-like or integrated in its informal structure and demonstrated how these structures affect an organizationâÂÂs ability to deal with crises. He also developed a set of graph theory-based measures of informal organizations for assessing an organizationâÂÂs ability to confront a variety of strategic issues. In social network theory, perhaps he is best known for his concepts of âÂÂPhilos tiesâ and the âÂÂSimmelian tieâÂÂ, which underscore the importance of, respectively, tie content and the local context in which network relations occur. Methodologically, his major contribution has been the development of the Multiple Regression Quadratic Assignment Procedure (MRQAP), a non-parametric approach to statistical analysis of network data.
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