Abstract
This paper summarizes a set of graph theory methods that are of special relevance to the computational analysis of neural connectivity patterns. Methods characterizing average measures of connectivity, similarity of connection patterns, connectedness and components, paths, walks and cycles, distances, cluster indices, ranges and shortcuts, and node and edge cut sets are introduced and discussed in a neurobiological context. A set of Matlab functions implementing these methods is available for download at http://php.indiana.edu/~osporns/graphmeasures.htm.
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Sporns, O. (2003). Graph Theory Methods for the Analysis of Neural Connectivity Patterns. In: Kötter, R. (eds) Neuroscience Databases. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1079-6_12
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DOI: https://doi.org/10.1007/978-1-4615-1079-6_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5384-3
Online ISBN: 978-1-4615-1079-6
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