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The notions of subgraph centrality and communicability, based on the exponential of the adjacency matrix of the underlying graph, have been effectively used in the analysis of undirected networks. In this paper we propose an extension of these measures to directed networks, and we apply them to the problem of ranking hubs and authorities. The extension is… (More)

We examine node centrality measures based on the notion of total communicability, defined in terms of the row sums of matrix functions of the adjacency matrix of the network. Our main focus is on the matrix exponential and the resolvent, which have natural interpretations in terms of walks on the underlying graph. While such measures have been used before… (More)

In a graph, a community may be loosely defined as a group of nodes that are more closely connected to one another than to the rest of the graph. One common theme is many formalizations is that flows should tend to stay within communities. Hence, we expect short cycles to play an important role. For undirected graphs, the importance of triangles – an… (More)

We consider a broad class of walk-based, parameterized node centrality measures based on functions of the adjacency matrix. These measures generalize various well-known centrality indices, including Katz and subgraph centrality. We show that the parameter can be " tuned " to interpolate between degree and eigenvector centrality, which appear as limiting… (More)

Node centrality measures including degree, eigenvector, Katz and subgraph central-ities are analyzed for both undirected and directed networks. We show how parameter-dependent measures, such as Katz and subgraph centrality, can be " tuned " to interpolate between degree and eigenvector centrality, which appear as limiting cases of the other measures. We… (More)

Many real-world networks have high clustering among vertices: vertices that share neighbors are often also directly connected to each other. A network's clustering can be a useful indicator of its connectedness and community structure. Algorithms for generating networks with high clustering have been developed, but typically rely on adding or removing edges… (More)

Many large, real-world complex networks have rich community structure that a network scientist seeks to understand. These communities may overlap or have intricate internal structure. Extracting communities with particular topological structure, even when they overlap with other communities, is a powerful capability that would provide novel avenues of… (More)

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