Author List: Trier, Matthias;
Information Systems Research, 2008, Volume 19, Issue 3, Page 335-350.
The capabilities offered by digital communication are leading to the evolution of new network structures that are grounded in communication patterns. As these structures are significant for organizations, much research has been devoted to understanding network dynamics in ongoing processes of electronic communication. A valuable method for this objective is Social Network Analysis. However, its current focus on quantifying and interpreting aggregated static relationship structures suffers from some limitations for the domain of analyzing online communication with high volatility and massive exchange of timed messages. To overcome these limitations, this paper presents a method for event-based dynamic network visualization and analysis together with its exploratory social network intelligence software Commetrix. Based on longitudinal data of corporate email communication, the paper demonstrates how exploration of animated graphs combined with measuring temporal network changes identifies measurement artifacts of static network analysis, describes community formation processes and network lifecycles, bridges actor level with network level analysis by analyzing the structural impact of actor activities, and measures how network structures react to external events. The methods and findings improve our understanding of dynamic phenomena in online communication and motivate novel metrics that complement Social Network Analysis.
Keywords: communities; dynamic network analysis; email; evolution; network dynamics; network visualization; online communication; social network analysis
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#249 0.263 network networks social analysis ties structure p2p exchange externalities individual impact peer-to-peer structural growth centrality participants sharing economic ownership embeddedness
#254 0.170 level levels higher patterns activity results structures lower evolution significant analysis degree data discussed implications stable cluster exist relationships identify
#128 0.144 dynamic time dynamics model change study data process different changes using longitudinal understanding decisions develop temporal reveal associated state identifies
#37 0.123 intelligence business discovery framework text knowledge new existing visualization based analyzing mining genetic algorithms related techniques large proposed novel artificial
#203 0.085 communication media computer-mediated e-mail richness electronic cmc mail medium message performance convergence used communications messages face-to-face findings participants results work
#188 0.080 processes interaction new interactions temporal structure research emergent process theory address temporally core discussion focuses area underlying deep structures way