Gephi

About Gephi

Gephi is an open-source network visualization software that is a powerful tool for researchers, data scientists, marketers, and social scientists. It is designed to handle large data sets and create various network visualizations to uncover complex systems’ underlying patterns, structures, and dynamics. Gephi is widely used for social network analysis, link analysis, and biological network analysis, among other applications.

What Can Gephi Be Used For?

Social Network Analysis: Understanding social structures through visualizing networks of individuals or groups and their interactions. Link Analysis: Identifying relationships and structures in data, including detecting communities, influencers, and the flow of information. Biological Network Analysis: Mapping the interactions between biological entities such as genes, proteins, or species to uncover biological processes and pathways. Marketing and Behavior Change: Analyzing customer networks to identify key influencers and target marketing efforts more effectively. Gephi can reveal how information spreads through networks, helping to craft strategies for behavior change campaigns or viral marketing.

Anecdotes

A digital marketing firm used Gephi to analyze the Twitter network of a major brand’s followers, identifying key influencers who were not previously recognized through traditional metrics. Engaging with these influencers significantly increased campaign reach and engagement. In a behavior change campaign aimed at promoting healthy habits, public health researchers used Gephi to map the social networks of community members. The analysis revealed unexpected pathways for information dissemination, allowing for a more targeted intervention strategy.

List of Terms

Nodes: The entities in the network (e.g., individuals, organizations, genes) represented as points. Edges: The connections between nodes, representing relationships or interactions. Centrality Measures: Metrics that identify the most important nodes within a network (e.g., degree centrality, betweenness centrality). Community Detection: The process of identifying clusters or groups of nodes that are more densely connected than the rest of the network. Modularity: A measure that quantifies the strength of the division of a network into modules (communities). Layout Algorithms: Techniques used to position nodes in the visualization space, emphasizing aspects of the network structure (e.g., Force Atlas 2, Fruchterman-Reingold). Layout Algorithms are techniques used to position nodes in the visualization space, emphasizing aspects of the network structure. These algorithms are pivotal in revealing networks’ underlying patterns and structures within networks by arranging nodes and edges. Force Atlas 2' is a force-directed layout that simulates a physical system to spatially separate all nodes equally, making clusters and dense regions more visible. It is best used for large networks where the general structure (clusters, dense regions) needs to be identified. Look for tightly knit clusters as indicators of closely related entities and sparse areas as potential community boundaries. Fruchterman-Reingold' is another force-directed algorithm that aims to minimize the overlap between nodes and distribute them evenly across the network. It is ideal for small to medium-sized networks where an aesthetically pleasing overview is desired. Look for the balance and distribution of nodes, which can help identify outliers and central nodes. Yifan Hu' Combines aspects of force-directed and multiscale algorithms to efficiently layout large networks. It is best for quick visualization of large datasets that still respect the network’s natural clustering. Look for the emergent global structure with visible local patterns. Circular Layout': Positions nodes in a circle, emphasizing the network’s connectivity rather than its clusters. It is best used for networks with regular structures or to highlight global connectivity patterns. Look for circular patterns of connectivity and outliers that break these patterns. Radial Axis': This arrangement of nodes around a central node emphasizes hierarchy or centrality. It is best used for networks with a clear hierarchical structure or to showcase the centrality of specific nodes. Look for layers or tiers of nodes, which indicate levels of hierarchy or influence. * Random Layout: Places nodes randomly within the visualization space. While not useful for identifying structure or patterns, it can be a starting point for applying other algorithms by disrupting existing layout biases. Look for the initial distribution of nodes before applying more structured layout algorithms. Dynamic Networks: Networks that change over time. Gephi supports the visualization and analysis of how networks evolve.

Gephi Cookbook PDF here

Installing Gephi

NIPR Install

NOTE: Ask your S6 to install Gephi. NIPR and SIPR

Commercial System Install

• Download Gephi

Interpretation and Reporting: Analyze the results to conclude your network. Look for patterns, such as clusters or key influencers, and consider their implications.

Advanced Techniques

Dynamic Networks: For networks that change over time, use GEXF to include time-series data. Gephi supports dynamic visualizations that can show how networks evolve.

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Basics

Gephi Quick Start Guide (here)(https://gephi.org/users/quick-start/) Learn how to use Gephi here

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Datasets

Gephi Basic Datasets repo Chinese Companies repo

Query for Datasets

Basic Advanced Google Query

"KEYWORD1" OR "KEYWORD2" filetype:GEXF OR filetype:GDF OR filetype:DOT OR filetype:GML OR filetype:GEPHI

Key Communicators

"key communicators" OR "influencers" OR "network leaders" filetype:GEXF OR filetype:GDF OR filetype:DOT OR filetype:GML OR filetype:GEPHI

Country Specific

(country1 OR country2) AND ("investment networks" OR "trade relations") filetype:GEXF OR filetype:GDF OR filetype:DOT OR filetype:GML OR filetype:GEPHI

Make Datasets

Creating your datasets for analysis with Gephi involves a process of data collection, cleaning, and formatting. The goal is to construct a network graph that accurately represents the relationships you wish to study. Here’s a general guide to making your datasets ready for Gephi:

Format for Gephi: Convert your data into a format compatible with Gephi. The most common formats are:

1. GEXF (Gephi Exchange Format): Suitable for complex networks with dynamic attributes.
2. GDF (Graph Definition File): A simpler, CSV-like format.
3. DOT: Useful for hierarchical or clustered network visualizations.
4. GML (Graph Modelling Language): A flexible format that supports hierarchical data.
5. GEPHI: The native project file format for Gephi is useful for saving all aspects of your project.