Dynamic Social Network Modeling and Analysis Workshop Summary and Papers (2003) / Chapter Skim
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Part II: Workshop Papers
Part II: Workshop Papers
Opening Address: Emergent Themes in Social Network Analysis: Results, Challenges, Opportunities
Pages 15-36
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PART II Workshop Papers Note: Part II contains the papers as submitted to the workshop.
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It may be said that kinship was the first social science, in that venous peoples' own cultural constructions of their networks of kinship relations have seemed always to mix observation with analysis and with proclamation (White, 1992: 290; Freeman, 20031. The 17th-century philosopher, Spinoza, derived scores of logically interrelated propositions about a system that has the characteristics of a basic social network situation, a system consisting of multiple relations such as loving, hatred, and envy—among multiple actors (notably an acting person, an other, and an object)
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that "in a complex society, networks are the minimum level at which social relations can be investigated." Four characteristics of the contemporary paradigm on network analysis are identified by Freeman (20031: motivation by a structural intuition about specific relationships linking social actors; grounding in systematic empirical data; heavy reliance on graphic imagery; and use of mathematical and/or computational models Results Especially from the 1960s onward, major advances as well as cumulative building of analytical methods and research findings have led to productive results in social network modeling. Overviews and full treatments are available elsewhere.S ~ will briefly identify six distinctive themes of network analysis that have emerged in recent decades.
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Statistical modeling of affiliation data has been developed and applied to study appearances of Soviet political elites at official and social events for ~ years during the Brezhnev era (Faust et al., 20021. Innovative sampling schemes and analytical frames have been developed for dual network situations (McPherson, 20011.
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Thus, the rise of the Medici corresponded to a network strategy in which their follwers were kept structurally isolated from one another (see also Padgett, 20011. In my summary characterization of the results of social network analysis, ~ have recourse to the subtitle of Wasserman and Faust's (1994)
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Second, because pi is essentially a null model, its ability to characterize actual network data was poor. Third, degrees of freedom for assessing, the overall fit of these model often depended on the number of actors in the network, thus violating the usual assumptions about asymptotics in maximum likelihood estimation.
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have explored various sorts of non-network information that can be incorporated into random-graph models—information such as actor attributes, affiliations of persons with groups, and spatial layouts. Research on the relation between geographic spatial arrangements and social networks is rapidly advancing DYNAMIC SOCIAL NETWORK MODEM ID ISIS 25
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At least implicitly, this work seems beneficial for the formulation of spatially oriented random graph models. Data Quality and Network Sampling Social networks have been measured in many ways, and the available research indicates that these can make some claim to being reliable.
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Challenges Just Over the Horizon ~ will now turn to scientific and research challenges that ~ envision as just beyond our current honzon. These concern efforts to significantly extend, or to generalize or to move beyond, the social networks paradigm as outlined in the first major section above.
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And the Workshop paper of Chnstos Faloutsos and his co-authors reviews a wide range of results concerning power laws applied to network data, in research that combines network analysis and graph mining. ~ believe that these papers should be read in conjunction with that of SnijUers, who argues (convincingly, in my view)
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We need to continue to improve methods for dealing with network sampling. We need to continue to explore ways to significantly extend, or to generalize, or to move beyond the static social networks paradigm, by embedding network models and research within the wider array of concerns that ~ have sketched in the preceding section.
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3 The International Network for Social Network Analysis (INSNA) , web address http://www.
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Dekker, Anthony (20011. "Applying Social Network Analysis Concepts to Military C4ISR Architectures." Connections 24 (31: 93-103.
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(20031. The HistorY of Social Network Analysis.
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Robins, Gary, Philippa Pattison, and Stanley Wasserman (1999~. "Logit Models and Logistic Regressions for Social Networks, HI: Valued Relations." PsYchometrika 64:371-394.
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