Clustering coefficient - probability that 2 nodes directly connected to a third node will also be directly linked to each other , Characteristic path length - minimum number of edges(links) required to link any two nodes together on average, Between-ness centrality - number of shortest paths that pass through a node (capacity of a node to influence the network), Nodal degree - number of connections of a node to other nodes, Hub - a node that has a large number of connections to which new nodes like to connect, Module - A subset of nodes that is more densely connected to each other than to nodes in any other module = intramodular connectivity. Modules are usually connected through hubs= hierarchical modular organisation which is a nearly universal feature of networks, Random graph - short path length and low clustering, global efficiency/general ability, Regular graph - for specific abilities, local, long path length, high clustering , Small-world network - optimal balance (high global and high local efficiency), broad abilities, support integration with low energy and wiring costs. modules, few long connections, useful for brain modelling , Graph theory - used to study functional and structural connectivity of brain networks e.g represent connection of areas studied in neuroimaging both functional and anatomical, used to study patterns of neural degeneration e.g in AD lower nodal degree in frontal and temporal regions,

Graph theory

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