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Network Analysis > Week 5: social network & measures > Flashcards

Week 5: social network & measures Flashcards

1
Q

What is the social brain hypothesis?

A

There is a correlation between brain size ang group size

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2
Q

What are the three degrees of influence?

A

Behaviour is influenced by peer, peer of peer and peer of peer of peer

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3
Q

What is Aij in a directed graph adjacency matrix?

A

When Aij=1, there is an edge from j to i

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4
Q

What is an acyclic network?

A

A directed network with no cycles. A cycle is a closed loop of edges with arrows pointing the same way.

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5
Q

What is a bipartite network?

A

Network with two kinds of nodes and edges that run only between nodes of different kinds

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6
Q

What are bipartite networks often used for?

A

Representing membership of groups

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7
Q

What are 6 types of node-level metrics?

A

Degree, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, page rank

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8
Q

What is the degree of a node?

A

The number of edges it has connected to it. Also indegree and outdegree

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9
Q

What is degree centrality?

A

The number of nodes adjacent to a given node

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10
Q

What is closeness centrality?

A

The inverse of the mean shortest distances from i to every other node in the network

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11
Q

What is betweenness centrality?

A

Number of times a node lies on the shortest path between two other nodes

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12
Q

What does betweenness centrality represent?

A

Nodes with high betweenness centrality are often important controllers of power or information

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13
Q

What is eigenvector centrality?

A

How central a node is depends on how central its neighbouring nodes are (weighted sum of the centralities of neighbouring nodes)

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14
Q

What is page rank?

A

Variant of eigenvector centrality, used by google. Having links to you from other important nodes increases your centrality, but if that important node sends out a lot of links, the importance passed on to you is weighted less

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15
Q

What are 6 network-level metrics?

A

Degree distribution, density, centralisation, components, communities, clustering

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16
Q

What is degree distribution?

A

The distribution of how to summarise the degree of all nodes of a network

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17
Q

What is the density of a network

A

The number of actual ties (m), divided by the number of possible ties in a network

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18
Q

How to calculated the density of directed and undirected networks?

A 
d(directed) = m/(n(n–1))
d(undirected) = m/(n(n–1)/2)
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19
Q

What does the density of a network tell us

A

Tells us about the redundancy of connections

20
Q

What happens to the density of a sparse network?

A

The density approaches 0 as n increases

21
Q

What is the centralisation of a network?

A

The extent to which a network revolves around a single node

22
Q

How is centralisation measured?

A

Standard deviation = (mean of degrees of all node – degrees of each node)^2 /n

23
Q

What is the diameter of a network?

A

The longest of the shortest path between two nodes

24
Q

What are components?

A

Clusters that are not connected to each other, there is no path between any pair of nodes in different components

25
Q

What is a connected network?

A

A network with only one component

26
Q

What are strongly connected nodes?

A

Nodes A and B are strongly connected if there is a directed path from A to B and B to A

27
Q

What are weakly connected nodes?

A

A and B are weakly connected if there is only a path between them in one direction

28
Q

What is a strongly connected component?

A

A component that contains strongly connected nodes

29
Q

What is an out-component?

A

The set of nodes that are reachable via directed paths starting from a specified node A, and including A itself

30
Q

What is an in-component?

A

The set of all nodes from which there is a directed path to A, including A itself

31
Q

What is a k-component?

A

A k-component is a set of nodes such that each is reachable from each of the others by at least k node-independent paths

32
Q

What are node-independent paths?

A

Two paths are node-independent if they share none of the same nodes except the starting and ending nodes

33
Q

What is a community?

A

Parts of the network in which the nodes are more connected to each other than to the rest of the network

34
Q

How do you find communities?

A

Community detection algorithms, you can use many criteria to design community detection algorithms

35
Q

What is an example of a real-world community?

A

Opinion-based communities: echo chambers - an environment where a person only encounters information or opinions that reflect and reinforce their own

36
Q

What is the local clustering coefficient of a node?

A

Quantifies how close its neighbours are to being fully connected. The average probability that a pair of i’s friends are friends of one another

37
Q

What is the global clustering coefficient of a network?

A

Counts the percentage of closed triangles in the whole network

38
Q

What is another name for clustering coefficient?

A

Transitivity

39
Q

What is transitivity in a network?

A

Transitivity in a network means that if node u is connected to node v any v is connected to w, then u is also connected to w

40
Q

What is partial transitivity?

A

Partial transitivity means that if u knows v and v knows w, it is more likely that u knows w

41
Q

What is a closed path?

A

A path is closed if it forms a loop

42
Q

What are two equations for the clustering coefficient of a network?

A 
C = number of closed paths of length two/number of paths of length two
C = number of triangles*6/number of paths of length two
43
Q

What is the equation for the local clustering coefficient?

A

For node i, Ci = number of pairs of neighbours of i that are connected/number of pairs of neighbours of i

44
Q

What is local clustering correlated with?

A

Local clustering and betweenness centrality are strongly correlated

45
Q

What is the redundancy of a node i?

A

The mean number of connections from a neighbour of i to other neighbours of i

Network Analysis (11 decks)

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