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

Relative Influence Of Network Effects In Actor‐based ModelsIndlekofer, Natalie; Brandes, UlrikNetwork DynamicsLongitudinal, Dynamic Network Analysis, Siena, Network Evolution Factors, Actor‐Based Stochastic Modeling, Model AssessmentTHURS.PM2Actor‐based models are designed for the statistical analysis of longitudinal social networks given as network panel‐data. The aim is to detect formation rules,called network effects, that govern the unobserved evolution between consecutive states. The evolution is assumed to be a sequence of single changesresulting from myopic actor decisions and is modeled by a parameterized Markov process with parameters indicating the influence of associated effects onnetwork dynamics. The interpretation of parameter estimates is mainly based on statistical significance and algebraic signs, while parameter sizes, althoughhighly informative, are often ignored since they are not comparable in terms of several aspects: The relation between parameter sizes and amount of influencevaries among network effects and model specifications and depends on data characteristics, such as network size and density. We introduce a measure thatdefines influences of included effects on instantaneous actor decisions. Applied to sequences of changes, sampled by MCMC methods, it reveals relativeinfluences on network dynamics. Since the measure depends not only on parameter sizes but also on associated effects and the current network, influences ofeffects are comparable across different models and data.Reliability And Robustness Of Network Analysis From Dynamic Social EventsOlson, Jamie F.; Carley, Kathleen M.Event‐based networksEntropy, Dynamic Network Analysis, Event Data, Data Reduction, Analytical MethodsFRI.AM2The propagation of uncertainty from dyadic observations to computed network analytic measures suggests serious concerns when those observations comefrom noisy and dynamic human social behavior. Networks are generally constructed by combining all relational events in a particular time interval to create anetwork connecting all individuals who interacted in that time interval. As the aggregate time interval increases, the larger number of observations decreasesthe dyadic variability which in turn percolates up, improving the reliability of an analysis of the network structure. However, this aggregation also introducesthe ecological fallacy, with the result that conclusions at the network level may not have meaning or validity at the underlying relational event level. We showhow information theory can be used as a heuristic to judge the risk of committing the ecological fallacy and to guide the choice of an aggregate time interval.Using real datasets from a variety of human social contexts, we compare the information lost to the reliability gained, illustrating this bias‐variance tradeoffquantitatively and (hopefully) enabling more informed aggregation decisions.