WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS Social Drivers: Why do we create in addition to sustain networks

WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS Social Drivers: Why do we create in addition to sustain networks www.phwiki.com

WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS Social Drivers: Why do we create in addition to sustain networks

DJ Mikee Mike,, Music Director has reference to this Academic Journal, PHwiki organized this Journal Noshir Contractor Professor, Departments of Speech Communication & Psychology Graduate School of Library & In as long as mation Science Director, Age of Networks Initiative, Center as long as Advanced Study Director, Science of Networks in Communities – National Center as long as Supercomputing Applications University of Illinois at Urbana-Champaign nosh@uiuc.edu MTML Models to Study the Emergence of Networks WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS Social Drivers: Why do we create in addition to sustain networks Theories of self-interest Theories of social in addition to resource exchange Theories of mutual interest in addition to collective action Theories of contagion Theories of balance Theories of homophily Theories of proximity Theories of co-evolution Sources: Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Ox as long as d University Press. Contractor, N. S., Wasserman, S. & Faust, K. (2006). Testing multi-theoretical multilevel hypotheses about organizational networks: An analytic framework in addition to empirical example. Academy of Management Review.

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“Structural signatures” of MTML Theories of Self interest Theories of Exchange Theories of Collective Action Theories of Balance Theories of Homophily Theories of Cognition What Have We Learned These Network Mechanisms Research typically looks at only one of these mechanisms, but when they look at multiple mechanisms . there is variation in the set of theoretical mechanisms that explain network emergence in different contexts. A contextual “meta-theory” of social drivers as long as creating in addition to sustaining communities

Core Research Social Drivers as long as Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Vodafone-Ericsson “Club” as long as virtual supply chain management (Vodafone) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Economic Resilience NGO Community (Rockefeller Program on Working Communities) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, Sony Online Entertainment) Science Applications CLEANER: Collaborative Large Engineering & Analysis Network as long as Environmental Research (NSF) CP2R: Collaboration as long as Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers as long as Communities Contextualizing Goals of Communities Challenges of empirically testing, extending, in addition to exploring theories about networks until now Enter: Cyberinfrastructure & Web 2.0

Science in addition to Engineering Cyberinfrastructures Multidimensional Networks in CI (Cyberinfrastructure) Multiple Types of Nodes in addition to Multiple Types of Relationships CLEANER Community: A multidimensional network

Its all about “Relational Metadata” Technologies that “capture” communities’ relational meta-data (Pingback in addition to trackback in interblog networks, blogrolls, data provenance) Technologies to “tag” communities’ relational metadata (from Dublin Core taxonomies to folksonomies (‘wisdom of crowds’) like Tagging pictures (Flickr) Social bookmarking (del.icio.us, LookupThis, BlinkList) Social citations (CiteULike.org) Social libraries (discogs.com, LibraryThing.com) Social shopping (SwagRoll, Kaboodle, thethingsiwant.com) Social networks (FOAF, XFN, MySpace, Facebook) Technologies to “manifest” communities’ relational metadata (Tagclouds, Recommender systems, Rating/Reputation systems, ISI’s HistCite, Network Visualization systems) MTML meets Web 2.0 (XML) Theorizing the creation, maintenance, dissolution in addition to reconstitution (CMDR) of network linkages between not just people but also sensors, data sets/streams, documents, in addition to visual-analytic tools Testing theoretical propositions about existing network configurations using unprecedented digital trace data Developing network recommender systems to assist members’ navigation of multidimensional networks Testing theoretical propositions about potential network reconfigurations by assessing members’ use (or non-use) of network recommendations. 1. Develop a meta-theory as long as the dynamics of networks (MTML) 2. Develop agent-based computational models to assess in addition to evaluate alternative trajectories of network dynamics (Repast/Blanche) 4. Deploying Web 2.0 in addition to Cyberinfrastructure to enable in addition to investigate networks (CI-IKNOW) 3. Collect/capture longitudinal empirical network data (Crawdad/D2K/Automap) 5. Statistical methods to empirically validate networks dynamics predicted by agent based models based on MTML theories (p /ERGM techniques using MCMC methods) Generative mechanisms Model predictions of networks Multi-level hypotheses in addition to concepts to be measured Design of Web 2.0/Cyberinfrastructure Web-based surveys, usage logs, text-mining, in addition to web-crawling tools to capture network dynamics Iterative refinements to theories about network dynamics FRAMEWORK FOR MODELING SOCIAL NETWORK DYNAMICS

Examples Santa Barbara Digital Transitions Forum Inter-organizational Network in response to Katrina Tobacco In as long as matics Grid – TobIG: The case as long as smokeless tobacco CI-Scope: Mapping Science of Cyberinfrastructure Bios, titles & descriptions Personal Web sites Google search results Web of Science Citation Digital Harvesting of Relational Metadata CI-KNOW Analyses in addition to Visualizations http://iknowinc.com/iknow/sb-digital- as long as um/www/iknow.cgi Text-mining Tools I CRAWDAD Steve Corman Arizona State University, Crawdad Technologies http://www.crawdadtech.com

Text-mining Tools II http://alg.ncsa.uiuc.edu/do/tools/d2k Loretta Auvil NCSA at University of Illinois at Urbana-Champaign Web-crawling tools UBERLINK http://voson.anu.edu.au/ Robert Ackl in addition to VOSON at Australian National University Data-sources as long as 29 as long as um panelist in addition to speakers Speaker short bios Speaker article titles in addition to /or full text of company descriptions Speaker personal website URLs Top-10 pages from Google as long as each of the speakers ISI-Web of Science citation data as long as speakers who are cited (N=14)

Santa Barbara Digital Transitions Demo CRAWDAD Speakers by Keywords multidimensional network CRAWDAD: Speakers sharing the same keywords

DJ Mikee Mike, KZON-FM Music Director www.phwiki.com

Core Research Social Drivers as long as Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Vodafone-Ericsson “Club” as long as virtual supply chain management (Vodafone) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Economic Resilience NGO Community (Rockefeller Program on Working Communities) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, Sony Online Entertainment) Science Applications CLEANER: Collaborative Large Engineering & Analysis Network as long as Environmental Research (NSF) CP2R: Collaboration as long as Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers as long as Communities Hurricane Katrina 2005 Formed: Aug 23, 2005 Dissipated: Aug 31, 2005 Highest wind: 175 mph Lowest press: 902 mbar Damages: $81.2 Billion Fatalities: >1,836 Areas affected: Bahamas, South Florida, Cuba, Louisiana (especially Greater New Orleans), Mississippi, Alabama, Florida Panh in addition to le, most of eastern North America Map source: http://hurricane.csc.noaa.gov/ Data in addition to picture source: http://en.wikipedia.org/wiki/Hurricane-Katrina/ 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 SITREP Content Basic Format / In as long as mation Situation (What, Where, in addition to When) Action in Progress Action Planned Probable Support Requirements in addition to /or Support Available Other items

Typical SITREP Human Coding Procedure Using an HTML editor to mark entities (people, organizations, locations, concepts) as bold in addition to include a unique HTML tag FEMA Automatic Coding D2K – The Data to Knowledge application environment is a rapid, flexible data mining in addition to machine learning system Automated processing is done through creating itineraries that combine processing modules into a workflow Developed by the Automated Learning Group at NCSA

Acknowledgements

DJ Mikee Mike, Music Director

DJ Mikee Mike, is from United States and they belong to KZON-FM and they are from  Phoenix, United States got related to this Particular Journal. and DJ Mikee Mike, deal with the subjects like Music; Music Programming

Journal Ratings by Gannon University

This Particular Journal got reviewed and rated by Gannon University and short form of this particular Institution is US and gave this Journal an Excellent Rating.