Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
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ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
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This work combines the central ideas from two different areas, crowd simulation and social network analysis, to tackle some existing problems in both areas from a new angle. We present a novel spatio-temporal social crowd simulation framework, Social Flocks, to revisit three essential research problems, (a) generation of social networks, (b) community detection in social networks, (c) modeling collective social behaviors in crowd simulation. Our framework produces social networks that satisfy the properties of high clustering coefficient, low average path length, and power-law degree distribution. It can also be exploited as a novel dynamic model for community detection. Finally our framework can be used to produce real-life collective social behaviors over crowds, including community-guided flocking, leader following, and spatio-social information propagation. Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial, temporal, and social factors on social networks. In addition, it provides an experimental platform of collective social behaviors for social gaming and movie animations. Social Flocks demo is at http://mslab.csie.ntu.edu.tw/socialflocks/ .