Sparse Approximations Of Directed Information Graphs
2016 IEEE International Symposium on Information Theory (ISIT)(2016)
摘要
Given a network of agents interacting over time, which few interactions best characterize the dynamics of the whole network? We propose an algorithm that finds the optimal sparse approximation of a network. The user controls the level of sparsity by specifying the total number of edges. The networks are represented using directed information graphs, a graphical model that depicts causal influences between agents in a network. Goodness of approximation is measured with Kullback-Leibler divergence. The algorithm finds the best approximation with no assumptions on the topology or the class of the joint distribution.
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关键词
sparse approximation,directed information graph,network dynamics,graph edge,graphical model,Kullback-Leibler divergence
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