Fig. 2. The so-called actor network quantifies the collaborative pattern of 382.000 actors participating in almost 128.000 movies. For visualization we have projected the data onto principal components (LSI) of the actor-actor co-variance matrix. The eigenvectors of this matrix are called ‘eigencasts’ and they represent characteristic communities of actors that tend to co-appear in movies. The network is extremely sparse, so the most prominent variance components are related to near-disjunct subcommunities of actors with many common movies. However, a close up of the coupling between two latent semantic components (the region ∼ (0, 0)) reveals the ubiquitous signature of a sparse linear mixture: A pronounced ‘ray’ structure emanating from (0,0). The ICA components are color coded. We speculate that the cognitive machinery developed for handling of independent events can also be used to locate independent sub-communities, hence, navigate complex social networks.
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