Irvine's new PhD program, now taking graduate student applications, in Social Dynamics and Evolution. This is a degree-granting Research Focus Group in the Mathematical Behavioral Sciences.
Structure and Dynamics of the
Global Economy: Network Analysis of International Trade 1965-1980
David A. Smith, Douglas R. White Social Forces, Vol. 70,
No. 4. (Jun., 1992), pp. 857-893.jstor
pw/GlobalEcon1992.pdf
1988 Large-Scale
Network of World Economy: Social scientists use the CRAY
Interview: Douglas R. White, David A. Smith. Science at
the San Diego Supercomputer Center 1987: 27-28
New Software and Results for regular equivalence (positional) analysis of input-output economic data and world trade
Thirteenth century world-system. A collaboration of Peter Spufford, Douglas White and Joseph Wehbe.
Theoretical background: Network Processes in Evolving Systems
Glossary: Analytical Concepts for Networks and Ethnography (for the book)
Bibliography (for the book) - covers alot of the new literature on networks
2002 Ulla Johansen and Douglas R. White, Collaborative Long-Term Ethnography and Longitudinal Social Analysis of a Nomadic Clan In Southeastern Turkey. Chapter 4, pp. 81-99, in Chronicling Cultures: Long-Term Field Research in Anthropology, edited by Robert van Kemper and Anya Royce. AltaMira Press.
2002 Douglas R. White and Michael Houseman The Navigability of Strong Ties: Small Worlds, Tie Strength and Network Topology, in Networks and Complexity Special Issue, Complexity 8(1):72-81. SFI Preprint eScholarship Reprint
Turkish Nomad Long-term study site
2003 Douglas R. White, Emergence, transformation and decay in pastoral nomad socio-natural systems. to appear in Emergence, Transformation and Decay in Socio-Natural Systems, edited by Sander van der Leeuw, Uno Svedin, Tim Kohler, and Dwight Read.
1997 Lilyan A. Brudner and Douglas R. White. Class, Property and Structural Endogamy: Visualizing Networked Histories Theory and Society 25:161-208.
2003 Douglas R. White. Social Scaling: From scale-free to stretched exponential models for scalar stress, hierarchy, levels and units in human and technological networks and evolution. ISCOM working paper, for submission to: Computer and Mathematical Organization Theory Download: 1982scalingDRW.pdf
Abstract: Johnson’s (1982) model of scalar stress deals with how networks are stacked at different levels to reduce information and energy load by substituting relationships among leaders of hierarchically ordered groups for relationships among members of larger groups at a lower level in the hierarchy. The logic and scaling results of this model are important elements in a theory of network and social scaling. They point to the possibility of scale-free modeling of the modularity of networks based on the relative constancy of the basic units at the individual level that give structure to these networks, the flexibility of how particular groups are organized, the fact that network hierarchies are population-filling with scale-free relationships to population size, and the bulking, organization and conservation of energy, information and material in ways that match the constraints on populations of individuals. These characteristics of scale-free modeling have been successful in biology, and social scaling may well follow the same principles. This article suggests the kinds of modifications that made be needed for larger-scale intergrative projects in social scaling.
Hierarchical and power law models have been much debated in recent decades and their limitations exposed. While Johnson’s work contains important insights, this paper examines new types of models that account for observed attenuations in the finite regimes of scale-free distributions (the stretched exponential model) and broken scale-free regimes. A combination of stretched exponentials and network modeling is found to be a productive approach to social and economic scaling that yields theoretical predictions about basal units, moments of distributions, regime attenuation and broken regimes.
Studies of scale-free, cutoff, and hierarchical properties of the U.S. airlines network in 1997 and a physics citation network are used to compare Johnson’s findings with basal unit and scale-free regimes in a more general scaling model that uses the stretched exponential. This model estimates hierarchy levels and basal unit characteristics and finds a similar basal unit of 6 for renormalization at a second level (hubs for local neighborhoods) in the airline industry, suggestive of Johnson’s results. The citation network suggests three-levels of multiplicative effects and a basal unit of 3 that is well under Johnson’s limit of 6 but constitutes a minimum unit of social cohesion.
2004 Walter W. Powell, Douglas R. White, Kenneth W. Koput and Jason Owen-Smith. Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences. Forthcoming: American Journal of Sociology Download: SFI-WP2003ajs.pdf
2003 Douglas R. White, Walter W. Powell, Jason Owen-Smith and James Moody Network Models and Organization Theory: from embeddedness to ridge structure. In preparation for Computational and Mathematical Organization Theory special issue on Mathematical Representations for the Analysis of Social Networks within and between Organizations, guest edited by Alessandro Lomi and Phillipa Pattison.
Link to the movie and graphics
2003 James Moody and Douglas R. White, Social Cohesion and Embeddedness: A Hierarchical Concept of Social Groups. American Sociological Review 68(1):1-25.
2001 Douglas R. White and Frank Harary, The Cohesiveness of Blocks in Social Networks: Node Connectivity and Conditional Density. Sociological Methodology 2001, vol. 31, no. 1, pp. 305-359. Blackwell Publishers, Inc., Boston, USA and Oxford, UK. SFI Posting
2003 Douglas R. White Network Analysis, Social Dynamics and Feedback in Social Systems. Cybernetics and Systems, online journal, forthcoming special issue. Edited by Dwight Read. Introduction by Murray Leaf
2003 Douglas R. White, Ties, Weak and Strong. Encyclopedia of Community Vol. 4:1376-1379. Edited by Karen Christensen and David Levinson. Thousand Oaks, CA: Sage Reference.
A fortran program to compute expected clustering coefficient following Bollobás's formula (2003) Douglas R. White coef.for, coef.exe
Using Galois Lattices to Represent
Network Data Linton C. Freeman, Douglas R. White Sociological
Methodology, Vol. 23. (1993), pp. 127-146. jstor
pw/Galois.pdf
Representing and Computing Kinship:
A New Approach Douglas R. White, Paul Jorion
Current Anthropology, Vol. 33, No. 4. (Aug. - Oct., 1992),
pp. 454-463. jstor
pw/White-Jorion1992.pdf
1999 On-line Controlled Simulation of Marriage Systems: J.Artificial Societies & Social Simulation 2(3)
New Book: Handbook_of_Graphs_and_Networks.pdf scan
The degree sequence of a scale-free random graph process
B. Bollobas, O. Riordan, G. Tusnary and J. Spencer. Random Structures and Algorithms, vol 18, 2001, 279-290.
Description: In modelling the web graph new vertices are joined to old vertices in proportion (maybe!)
to the current degrees of the old vertices so that the rich get richer.
Analyzing this process leads to some intriguing power laws.
Linearized chord diagrams and an upper bound for vassiliev invariants Béla Bollobás and Oliver Riordan, Journal of Knot Theory and Its Ramifications, Vol. 9, No. 7 (2000) 847-853
Luis Bettencourt and David I. Kaiser QUIKTIME Graphing the [Feynman] Graphers: how are ideas
created and how they spread; An example from the history of Theoretical Physics
N.B. this file is too big to run on the web so right click and copy to your directory and open from there!!!
Johnson, Gregory 1982 Organizational Structure and Scalar Stress This pdf was made from scanned versions - if you are dubious about the accuracy of the scan, check the following: GAJ7-12.pdf GAJ7-12.pdf GAJ13-17.pdf . In Theory and Explanation in Archaeology: The Southampton Conference, Colin Renfrew, Michael Rowlands and Barbara A. Segraves-Whallon, Editors pp. 397-421. Academic Press. (mentioned in Sander's talk)
1978 Johnson, Gregory Information Sources and the Development of Decision-Making Organizations. In Social Archaeology: Beyond Subsistence and Dating, Redman, C.L. 87-112. New York: Academic Press, 1978. file is huge so download first and then print
Two that cite Johnson: a paper by Tim Kohler
Ecology, Group Formation and Group Size as factors of Coalitional Psychology By Eric Schniter
An important counter to the claim that scale-free networks arise only by growth processes: A steady state model for graph power laws pdf David Eppstein, Joseph Wang Abstract: Power law distribution seems to be an important characteristic of web graphs. Several existing web graph models generate power law graphs by adding new vertices and non-uniform edge connectivities to existing graphs. Researchers have conjectured that preferential connectivity and incremental growth are both required for the power law distribution. In this paper, we propose a different web graph model with power law distribution that does not require incremental growth. We also provide a comparison of our model with several others in their ability to predict web graph clustering behavior.
EppsteinPowerLawGenerator Scott White, JUNG programming system in java (see Tim Evans email)
EppsteinPowerLawGenerator Scott White, JUNG programming system in java (see Tim Evans email)
Luis Bettencourt Tipping the balances of a small world
From boom to bust and back again: the complex dynamics of trends and fashions
links to bibliography of Scaling
Re-examination of the 3/4 law of metabolism, Dodds, Rothman, Weitz. Journal of Theoretical Biology 209 (2001)
Scale-free and hierarchical structures in complex networks Barabasi, Dezso, Ravasz, Yook and Oltvai
A Random Graph Model for Massive GraphsAiello, Chung, and Lu
The Small-World of Human Language Ramon Ferrer and Ricard V. Sole Proc. Roy. Soc. London B 268 (2001) 2261-2266
Scale-Free Behavior in Protein Domain Networks Stefan Wuchty
Orthologous enrichment in protein netwo Wuchty et al
2002 S. Wuchty, "Interaction and Domain Networks of Yeast", Proteomics, 2(12), 1715-1723 [pdf]
2003 S. Wuchty, "Small-Worlds in RNA", Nucl. Acids Res., 31, 1108 - 1117 [pdf]
2003 S. Wuchty and P.F. Stadler, "Centers of large networks", J. theoret. Biol., 223, 45-53 [pdf]
The large-scale organization of metabolic networks (2000)
Growth dynamics of the World-Wide LA Adamic and BA Huberman
Small World patterns in Food Webs Montoya and Sole
Food web complexity and higher-level ecosystem services
(very very partial start for articles)-please contribute
Why social networks are different
from other types of networks 2003 M. E. J. Newman, Juyong Park. mejn@umich.edu.
Condensed Matter, abstract http://aps.arxiv.org/abs/cond-mat/0305612/
Degree correlation: the product of nodal degree multiplied by excess or deficit of observed over expected edge frequency,
i.e., that hubs tend to connect. Negative degree correlation occurs when hub-to-hub connection is avoided and hubs
tend to connect to nodes with low degree. Note that positive degree correlation k-cone connectivity ought to correlate.
references sent to the group in early OCTOBER 2003
[1]Jean Laherrère, D. Sornette. 1998. Stretched exponential distributions in Nature and Economy: ``Fat tails'' with characteristic scales. Eur.Phys.J. B2: 525-539. http://arxiv.org/abs/cond-mat/9801293
[2] U. Frisch, D. Sornette 1997. Extreme deviations and applications J. Phys. I France 7, 1155-1171. http://arxiv.org/abs/cond-mat/9705132
[3] D. Sornette. 1998. Multiplicative processes and power laws. Phys. Rev. E 57 N4, 4811-4813. http://arxiv.org/abs/cond-mat/9708231
[4] A.V. Goltsev, S.N. Dorogovtsev, J.F.F. Mendes. 2003. Critical phenomena in networks Phys. Rev. E 67, 026123. http://arxiv.org/abs/cond-mat/0204596
[5] M. Mitzenmacher. 2001. A Brief History of Generative Models for Power Law and Lognormal Distributions. Internet Miathematics http://www.eecs.harvard.edu/~michaelm/NEWWORK/postscripts/history-revised.pdf
Evolution of Networks Dorogovtsev: USEFUL PAPERS ON NETWORKS. overview BOOK: S.N. Dorogovtsev and J.F.F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford University Press, Oxford, ISBN: 0198515901, 31 January 2003, 288 pp., 117 line illus. Synopsis: This text provides a concise, informative introduction to the principles of the organization and evolution of both natural and artificial networks. These are new concepts, based on the latest progress in network science. The book is written by physicists and is addressed to all researchers involved in the field and students. The aim of the text is to understand the generic principles of the complex organization of diverse networks: the Internet and World Wide Web, cellular networks, social nets, and many others. The ideas are presented in a clear and a pedagogical way, with minimal mathematics, so even students without a deep knowledge of mathematics and statistical physics will be able to rely on this as a reference. Special attention is given to real networks. Collected empirical data and numerous real applications of existing concepts are discussed in detail, as well as the topical problems of communication and other networks.
Biology-Inspired techniques for Self-Organization in dynamic Networks Bologna, SFI, EU
COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION XIAO FAN WANG INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS Volume 12 · Number 5 · May 2002 http://www.worldscinet.com/ijbc/12/1205/S0218127402004802ref.html
Complex Real-World Networks European Physical Society meeting, 2002