Social Network Analysis in Gephi

Diliara Valeeva

University of Warsaw | 18 April 2024

Introductions

Plan for today

1. Basic network terminology

2. Key network actors

3. Communities

4. Visualization

What is a network?

Nodes

are entities with the network

e.g. people

organizations

countries

Edges

are connections or relationships between the nodes

e.g. friendship

communication

a business transaction

Political blogs

Adamic & Glance (2005)

Racial segregation in school

Moody (2001)

Kilpatrick & Randolph (2012)

Airline network

Network types

Directed networks

Directed: edges have a direction, indicating the relationship flows from one node to another (e.g. Twitter followers, sanctions between countries)

* opposite is undirected

directed

undirected

Weighted networks

Weighted: edges carry a value that represents the strength of interaction between nodes (e.g. the number of emails exchanged between co-workers)

* opposite is unweighted

unweighted

weighted

One-mode and two-mode

One-mode: has only one type of node, and all connections occur between these similar nodes e.g. friendship

Two-mode: has two different types of nodes, and connections occur between nodes of different types e.g. countries and international organizations

one-mode

two-mode

Ego-networks

Ego-networks: focus on a single node (the ego) and all the nodes to which it is directly connected, as well as the connections between them e.g. ego-network of a country and its trade treaties with other countries

ego

Signed networks

Signed networks: edges have values that denote positive or negative relationships e.g. social media likes and dislikes

... and many more

Title Text

Data

#biden2020

#bloomberg2020

#buttigieg2020

#gabbard2020

#klobuchar2020

#sanders2020

#steyer2020

#warren2020

#trump2020

#weld2020

#biden2020

#bloomberg2020

#buttigieg2020

#gabbard2020

#klobuchar2020

#sanders2020

#steyer2020

#warren2020

#trump2020

#weld2020

node: user

edge: retweet

hashtag

How is 2020 US presidency

discussion network structured?

Download datasets

at http://bit.ly/TwitterGephi

Nodelist

Edgelist

Gephi Tutorial

Step 1:

Create a Gephi project

Title Text

By SlvrKy - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=50571810

Title Text

for network statistics

for data import/export

for adjusting visualization

Step 2:

First network visualization

Step 3:

Network centralities

Image source:  https://medium.com/the-xplane-collection/anatomy-of-a-social-network-de73f1224ac5

Degree centrality of a node is the number of connections it has to other nodes

Degree centrality

In directed networks, degree centrality is a sum of indegree and outdegree

Indegree centrality: number of incoming ties

Outdegree centrality: number of outcoming ties

Degree centrality

In many real-world social networks, the degree distribution follows a power law

It means that most nodes have relatively few connections, but a few nodes (hubs) have a large number of connections

This is often referred to as a scale-free network

Typical degree distribution

Closeness centrality is the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the network.

Closeness centrality

Shortest path: the minimum path of edges that must be traversed in a network to travel from one node to another.

Network paths

Shortest path: the minimum path of edges that must be traversed in a network to travel from one node to another.

10

Network paths

Shortest path: the minimum path of edges that must be traversed in a network to travel from one node to another.

9

10

Network paths

Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes in the network

Betweenness centrality

Average path length is the average number of steps along the shortest paths for all possible pairs of network nodes. Most real networks have a very short average path length

Network paths

Network diameter is the longest of all the shortest paths between any pair of nodes in the network

Continue working on Step 2

and explore the most central nodes

Small world networks

Have you ever heard about the six handshakes rule?

Small-world networks have high clustering coefficient and close distances.

High clustering: high probability that two friends of one person are friends themselves.

Close distances: there is a short path of connections between any two people

Small world networks

Step 4:

Update network visualization

Step 5:

Filters

Step 6:

Network community detection

Network communities

Community detection helps in identifying the clusters of nodes that are more densely connected to each other than to other nodes in the network

Modularity detects network communities

High modularity: dense intra-community, sparse inter-community ties

Qualitative interpretation is the key to community detection

democrats

Trump-related

influencers

news media

mixed

Step 7:

Final touches

What we did today

1. Basic network terminology

2. Key network actors

3. Communities

4. Visualization

Would like to learn more?

Check "Awesome Network Analysis" list of resources:

https://github.com/briatte/awesome-network-analysis