Network Analysis: Methodological Foundations

By Chris Arney. 2012

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b63088f825a78919fa9a10c2ee5ef565459d5268

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Citations in the corpus

Social network analysis and the evaluation of leadership networks (2010, background): “Formal mathematical definitions of clusters and algorithms for finding clusters are reviewed by Brandes and Erlebach (2005).”
Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities (2011, background): “A. Data Collection To measure the number vulnerabilities fixed in a file, we counted the number of bug reports that include the details on vulnerabilities and on how the vulnerabilities have been fixed for the file.”
The rise of Network Ecology: Maps of the topic diversity and scientific collaboration (2013, background): “Fundamentally, network models map a relationship(s) among a set of objects or actors (Higashi and Burns, 1991; Wasserman and Faust, 1994; Newman, 2003; Brandes and Erlebach, 2005).”
Systems Science Methods in Public Health (2012, unknown): “Brandes U, Erlebach T. 2005.”
Route Planning in Transportation Networks (2015, methodology): “Like other centrality measures [54], reach captures the importance of vertices in the graph, with the advantage that it can be used to prune a Dijkstra-based search.”
Molecular ecological network analyses (2012, unknown): “[34]”
Structural Properties of the Caenorhabditis elegans Neuronal Network (2009, background): “sider how quickly individual neurons reach all other neurons in the network. The normalized closeness of a neuron i is the average geodesic distance dij across all neurons j that are reachable from i [47]: d avg(i) = 1 N −1 X j:j6= i dij. (4) The normalized closeness centrality, which takes higher v”
Clustering attributed graphs: Models, measures and methods (2015, methodology): “To assess quality, Gaertler (2005) uses two functions, f (C) and g (C), to measure, respectively, the density and the sparsity of the clustering.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “Despite the popularity and simplicity of analyzing binary graphs, weighted generalizations for many commonly used graph theoretic metrics now exist and are being used with increasing frequency in imaging connectomics (Brandes and Erlebach, 2005; Rubinov and Sporns, 2011).”
Connectivity for conservation: a framework to classify network measures. (2011, background): “…applies graph theory with a focus on properties of real-world networks (e.g., social networks, transportation systems, communication networks, and epidemiology; Brandes and Erlebach 2005), their structural dynamics, and the relationship between their structure and function (Newman et al. 2006).”