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- Michele Benzi, Christine Klymko
- J. Complex Networks
- 2013

We examine a node centrality measure based on the notion of total communicability, defined in terms of the row sums of the exponential of the adjacency matrix of the network. We argue that this is a natural metric for ranking nodes in a network, and we point out that it can be computed very rapidly even in the case of large networks. Furthermore, we propose… (More)

- Michele Benzi, Ernesto Estrada, Christine Klymko
- ArXiv
- 2012

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)

- Christine Klymko, Blair D. Sullivan, Travis S. Humble
- Quantum Information Processing
- 2014

- Michele Benzi, Christine Klymko
- SIAM J. Matrix Analysis Applications
- 2015

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)

- Christine Klymko, David F. Gleich, Tamara G. Kolda
- ArXiv
- 2014

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)

- Michele Benzi, Christine Klymko
- ArXiv
- 2013

Node centrality measures including degree, eigenvector, Katz and subgraph centralities 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)

- Jeff Alstott, Christine Klymko, Pamela B. Pyzza, Mary Radcliffe
- ArXiv
- 2016

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)

- Tahsin Reza, Christine Klymko, Matei Ripeanu, Geoffrey Sanders, Roger A. Pearce
- CLUSTER
- 2017

We consider a broad class of walk-based, parameterized node centrality measures for network analysis. These measures are expressed in terms of functions of the adjacency matrix and 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… (More)

- CENTRALITY MEASURES, MICHELE BENZI, CHRISTINE KLYMKO
- 2015

We consider a broad class of walk-based, parameterized node centrality measures for network analysis. These measures are expressed in terms of functions of the adjacency matrix and generalize various well-known centrality indices, including Katz and subgraph centralities. We show that the parameter can be “tuned” to interpolate between degree and… (More)