# Networks

An introduction

Emmanouil Tranos

University of Bristol, Alan Turing Institute
e.tranos@bristol.ac.uk, @EmmanouilTranos, etranos.info

## Outline

• Why networks?

• Network Data

• Network Measures

## Why Networks

• Networks are everywhere!

• The focus is not on individual attributes, but…

• … on who is connected to whom.

• AKA networks are about relationships

• Interactions, or

• flows between nodes (actors, places, …)

• Social Network Analysis, Graph Theory, Network Science and Complexity Theory

## Scale and levels

Level (aggregation of linkages and nodes) Geographical scale (network boundary)
1 – Local 2 – Regional 3 – Global
A -- Macro Cities City as a whole entity Central place system
B -- Meso Firms Clusters (Porter 2000) Regional economic intergration and functional polycentrism (e.g. Scott 2001: Boschma 2005) Global inter-firm network (e.g. Cohen 1981)
C – Micro People A neighbourly economic suport support network (e.g. Wellman 1979) A local labou market (e.g. Granovetter 1974) The global economic elite (Sassen 1991)

Source:

Source: http://www.opte.org/

# Network data

## 1. Directed, non-weighted networks1

• Basic elements: nodes and links

• Data is not arranged in vectors but on square matrices

• 1 and 0 represent the existence of a link

 A B C D A 0 1 1 1 B 0 0 1 0 C 0 0 0 1 D 0 0 0 0

## 1. Directed, non-weighted networks

• Basic elements: nodes and links

• Data is not arranged in vectors but on square matrices

• 1 and 0 represent the existence of a link

Edge list

i j w
A B 1
A C 1
A D 1
B C 1
C D 1
D C 1

## 1. Directed, non-weighted networks

• Basic elements: nodes and links

• Data is not arranged in vectors but on square matrices

• 1 and 0 represent the existence of a link

## 2. Undirected, non-weighted networks

• Symmetrical ties

 A B C D A 0 1 1 1 B 1 0 1 0 C 1 1 0 1 D 1 0 1 0

Edge list

i j w
A B 1
A C 1
A D 1
B A 1
B C 1
C A 1
C B 1
C D 1
D A 1
D C 1

## 3. Directed, weighted networks

Links are characterized by weights i.e. strength of a friendship, commuting flows, trade flows

 A B C D A 0 3 1 5 B 0 0 1 0 C 0 0 0 6 D 0 0 7 0

## 3. Directed, weighted networks

Links are characterized by weights i.e. strength of a friendship, commuting flows, trade flows

Edge list

i j w
A B 3
A C 1
A D 5
B C 1
C D 6
D C 7

## 4. Undirected, weighted networks

Links are characterized by weights i.e. strength of a friendship, commuting flows, trade flows…

… and also have symmetrical matrices

 A B C D A 0 3 1 5 B 3 0 1 0 C 1 1 0 6 D 5 0 6 0

Edge list

i j w
A B 3
A C 1
A D 5
B C 1
B A 3
C D 6
C A 1
C B 1
D C 6
D A 5

## Network data: summary

• 4 possible combinations of network data

1. Directional and non-weighted (or binary)

2. Directional and weighted

3. Undirected and non-weighted (or binary)

4. Undirected and weighted

• Nodes, aka vertices (singular: vertex)

• Links or ties, edges or arcs

• Conventional data: actors and attributes

• Network data: actors and their relations

# Network measures

## Network size

• $k$ numbers of nodes

• $k(k-1)$ for directed

• $k(k-1)/2$ for undirected networks

## Network density

• $L/k(k-1)$ for directed

• $2L/k(k-1)$ for undirected networks

## Network distance

A macro characteristic of the network ➔ efficiency

• Multiple paths between two nodes

• A walk is a path in which each node and each link are used once

• Geodesic distance: the number of relations in the shortest possible path from one node to another

• Diameter: the longest geodesic distance in the network

## Reciprocity

• The extent to which ties are reciprocated (if A ➔ B, then B ➔ A)

• At least two different approaches:

• Reciprocated ties / maximum number of ties (0.33)

• Reciprocated ties / number of existing ties (0.5)

## Assortativity

• A bias towards connections between network nodes with similar network characteristics

• Assortativity coefficient is the Pearson correlation coefficient of the degree centralities of pairs of connected nodes

# Sub-structures

• dyads: pair of connected nodes

• ego-networks

• cliques: the maximum number of actors who have all possible ties present among themselves; a maximal complete sub-graph

## Clustering

• Large proportion of links are highly “clustered” into local neighborhoods ➔ small worlds

• $C_{i} = 2 E_{i} / k_{i}(k_{i}-1)$

• the ratio between the number of edges $E_{i}$ that exist among $i$’s nearest neighbors and the maximum number of these edges, where $k_{i}$ is the number of nodes in the sub-net

• AKA Transitivity

## Community detection

• Exploratory process

• The emphasis is not on the behaviour of single entities, but on the information of who is connected to whom

• “The problem of community detection requires the partition of a network into communities of densely connected nodes, with the nodes belonging to different communities being only sparsely connected”

• Clusters of nodes with dense connections inside the clusters, but not between the clusters

## Community detection

• Louvain method : maximization of links inside the communities (modularity)

• Network of communities extracted from a Belgian mobile phone network

• Node size proportional to the number of individuals in the community

• Colour represents the main language spoken in the community (red for French and green for Dutch)

• What does the intermediate community of mixed colours between the two main clusters represent?

## Community detection

Redrawing the Map of Great Britain from a Network of Human Interactions

• Do administrative boundaries follow the more natural ways people interact across space?

• Large telecommunications database in Great Britain.

# Centrality measures

• In social networks centrality is related with power, e.g. dominance and dependence

• Micro and macro property

• Position in the network

• Many centrality measures

Source: schochastics.net

## Degree centrality for undirected networks

• The simplest centrality measure

• Who has the most connections?

• The number of links per node

• $D_{i} = \sum_{j}^{N} x_{ij}= \sum_{j}^{N} x_{ji}$

## Degree centrality for directed networks

• Indegree: the number of ingoing links per node $D_{i} = \sum_{j}^{N} x_{ji}$

• Outdegree: the number of outgoing links per node $D_{i} = \sum_{j}^{N} x_{ij}$

## Degree centrality for weighted networks

$D_{i} = \sum_{j}^{N} w_{ij} = \sum_{j}^{N} w_{ji}$

$w$ denotes the weight of the links

## Degree cenralities

A B C D out-degree
A 0 3 1 5 9
B 3 0 1 0 4
C 2 4 0 9 15
D 7 0 5 0 12
in-degree 12 7 7 14

## Closeness centrality

• Which node has the shortest distance to other nodes

• Instead of focusing on the number of links, the focus turns to the network distances

• Different definitions:

• Closeness, $c_{i} = 1/\sum_{j} d_{ij}$

• Fareness, $c_{i} = \sum_{j} d_{ij}$

• igraph calculates closeness

## Betweenness centrality

• Which nodes control information flows

• How many geodesic links between actors $i$ and $j$ contain actor $k$?

$B_{k} = \sum_{i \not=j \not=k} \frac{g_{ij}(k)}{g_{ij}}$

## Eigenvector centrality

• Not all connections are equal

• Connections to more central vertices are more important than connections to less central ones

• It is still important to have a large number of connections, but a node with fewer and more important connections will end up being more central than a node with more and less important links

• Global structure of the network: both direct links and shortest paths

• Eigenvector of the adjacency matrix associated with the larger eigenvalue

## PageRank centrality

• The basis of Google search engine

• Webpages that link to $i$, and have high PageRank scores themselves, should be given more weight.

• Webpages that link to $i$, but link to a lot of other webpages in general, should be given less weight.

• Directed networks

• Iterative algorithm

## Centralities

• While easy to identify in simple graphs, much more difficult in large, complex networks

• Various other centrality measures exist (e.g. see igraph)

• Highlight different aspects of the distribution and sources of power

Source: Tranos, Gheasi, and Nijkamp (2015)

## Other types of networks

• Two mode networks

• Multilayer

• Dynamic

• Spatial