Sunbelt XXXI International Network for Social Network ... - INSNA

Dyadic Reciprocity And The Emergence Of Degree‐assortativity In Weighted Social NetworksToroczkai, Zoltan ; Hachen, David; Lizardo , Omar ; Strathman, Anthony; Kim, H; Wang, ChengAgent‐Based Models and Multi‐Agent SystemsNetwork Dynamics, Network Mechanisms, Agent Based Models, Real World Networks, Weblogs, MixingTHURS.PM2Social networks are characterized by the fact that they exhibit positive degree correlation. This is in contrast to most non‐social networks which tend instead todisplay disassortative mixing by degree. In this paper we propose a simple agent‐based simulation model that generates levels of degree‐assortativitycomparable to those observed in human social networks using a set of minimal dyadic mechanisms of tie selection and tie dissolution based on the reciprocityof weighted links. In our model, weighted reciprocity (the difference in volume of communication going from one actor to another within the dyad) providesthe criterion that agents use in deciding whether to keep or dissolve a tie. Analysis of the model's dynamics reveals that positive degree‐assortativity regimeemerges as a natural outcome of the decentralized attempts of agents to minimize non‐reciprocity within their immediate neighborhood. This emergentequilibrium is robust to variations in initial conditions and also reproduces degree distributions that are characteristic of human social networks. We introducea new quantity, which we label the total energy of the system that is useful in quantifying the total expected relationship volatility in the network. In ourmodel, regimes that exhibit positive degree correlation are ones that minimize the system's total energy whenever agents prefer reciprocity in their localneighborhood.Dynamic SNA Via Text Mining Of An Online Corpus With Alchemy And GephiLevallois, Clement; Smidts, Ale; Wouters, PaulInnovation, Diffusion, and the Adoption of TechnologyDynamic Network Analysis, Text Mining, Word Cooccurrence Networks, Gephi, Neuromarketing, AlchemyTHURS.PM2Research question: Is it possible to reveal the social, institutional and semantic networks supporting a technological innovation by looking at the public onlinetext record? Data: Records of all webpages citing “neuromarketing” (blogs, personal and institutional websites, online newspapers and magazines, etc.).Harvested in 2009, contains 4500+ documents, 10Gb large. Methods: Html pages have been pre‐processed with Alchemy to retain ascii text only. A humancoder (man‐hours: 320) and Alchemy were used to tag a number of fields: date, type of source, names cited in the document, organizations cited in thedocument, brands cited in the document. Preliminary analysis is conducted with Lexico3, Voyeur and Lingpipe. From here we select key actors, organizationsand concepts and trace their co‐occurrences through time in the corpus. These 3 datasets are imported in Gephi (dynamic network visualization) and furtheranalysis is performed with Gephi and UCINET. Results: By considering the evolution of these 3 networks (social, institutional and semantic) extracted by textmining,we identify key features of a technological innovation: ‐ Are the main players in neuromarketing academics or business persons? How do they cluster? ‐What industries make use of neuromarketing, and do they relate to the same neuromarketing labs? ‐ How did the public opinion regarding neuromarketingevolve since 1999?