week 2 - graph theory Flashcards

1
Q

what is network topology?

A

properties of a network that are unrelated to where the nodes are in space, instead it just matters the connection between nodes and edges

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2
Q

What are the different types of graph theoretical measures?

A

Local - properties of each node e.g degree

Meso-scale - properties of sub networks

Global network properties

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3
Q

what is edge density?

A

num actual edges/ num possible edges

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4
Q

how to calculate number of possible edges?

A

N*(N-1)/2
(removes the diagonal and the lower triangular part)

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5
Q

what are components in the network?

A

sets of nodes that aren’t connected to any other components

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6
Q

what is average edge weight?

A

the average of existing edges within the upper triangular

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7
Q

what is degree?

A

number of edges connecting a node

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8
Q

what is the strength of a node?

A

sum of edge weights connected to a node?

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9
Q

What is eigenvector centrality?

A

To what extent is the node connected to other nodes with high eigenvector centrality

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10
Q

what is path length?

A

the number of edges between two nodes

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11
Q

what is characteristic path length?

A

the average path length between all node pairs

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12
Q

what is global efficiency?

A

average inverse shortest path length

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13
Q

what is betweenness centrality?

A

the fraction of shortest paths in an entire network that pass through a given node

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14
Q

describe the spectrum of communication processes

A

from diffusion to routing

In diffusion, the structure of the network is unknown and the walk through the network is random

In routing you take the shortest path, this requires full knowledge of the network topology

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15
Q

what is the clustering coefficient?

A

the fraction of a nodes neighbours that are connected

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16
Q

what is the global clustering coefficient?

A

the average nodal clustering coefficient

17
Q

what is transitivity?

A

the ratio of triangles to triplets in the network

18
Q

what is a module? what about in fmri

A

subnetworks of nodes that are very densely connected with each other and sparesly connected to other modules

in fMRI this is the resting state networks e.g DMN, DAN

19
Q

what are hub nodes?

A

Important nodes that are very highly connected

connector hubs = connections with nodes in different communities

Provincial hubs = have many connections with different communities

20
Q

what is rich club?

A

A set of nodes that have high degree that are highly connected to each other

rich clubs tend to integrate modules together

21
Q

what is segregation? and measures related

A

locally, densely connected clusters of nodes

clustering, transivity and modularity

22
Q

what is integration?

A

the ability of