Research and Data Analysis

Network Analysis and Graph Theory in Social Science Research Training Course

Introduction

In today’s data-driven world, social science research is rapidly evolving with the integration of network analysis and graph theory techniques to uncover relationships, influence patterns, and systemic structures in human behavior and societal dynamics. Network Analysis and Graph Theory in Social Science Research Training Course is designed to equip researchers, data analysts, policy makers, and academic professionals with cutting-edge methodologies to map, visualize, and analyze complex social networks using graph-theoretical concepts. Participants will explore the powerful intersection of quantitative analysis and sociological insights, enabling a deeper understanding of power dynamics, information flow, and community detection in various social contexts.

The course blends interactive learning with hands-on practical applications, using popular network analysis tools such as Gephi, UCINET, Pajek, and Python’s NetworkX. Whether examining communication in organizations, tracking social movements, or analyzing online behavior, this training empowers learners to harness graph-based modeling, uncover hidden structures, and generate data-driven insights for real-world challenges in political science, economics, public health, and beyond.

Programme Curriculum

Network Analysis and Graph Theory in Social Science Research Training Course

Introduction

Course Objectives

Understand the foundational concepts of network analysis and graph theory in social science.
Define and analyze nodes, edges, and graphs in various sociological contexts.
Apply centrality measures (degree, closeness, betweenness) for influence analysis.
Conduct community detection and clustering in social networks.
Utilize Gephi and NetworkX for visualizing and interpreting graph data.
Implement bipartite, multiplex, and dynamic networks for advanced modeling.
Apply social network analysis (SNA) to organizational and behavioral studies.
Integrate graph metrics into qualitative and quantitative mixed-method research.
Analyze real-time social data from social media networks and online communities.
Evaluate policy networks and stakeholder mapping using graph theory.
Enhance predictive modeling using network topology and graph analytics.
Interpret ethical and practical implications of network-based research.
Design and execute independent graph-theory-based research projects.

Target Audience

Social Science Researchers
Data Scientists and Analysts
University Lecturers and Academicians
Government Policy Planners
NGO and Advocacy Organization Staff
Graduate Students in Political Science, Sociology, or Economics
Public Health Researchers
Journalists and Media Analysts

Course Duration: 5 days

Course Modules

Module 1: Introduction to Network Analysis and Graph Theory

Definitions and terminology in networks and graphs
Relevance of networks in social science
Types of networks: directed, undirected, weighted
Basic graph visualization principles
Overview of graph analysis tools
Case Study: Mapping communication networks in a university department

Module 2: Network Structures and Properties

Graph metrics: degree, path length, density
Structural holes and network cohesion
Connected components and bridges
Role of weak ties in social networks
Identifying influencers in a network
Case Study: Analyzing friendship networks among high school students

Module 3: Centrality Measures and Their Interpretations

Degree, closeness, betweenness, eigenvector centrality
Measuring influence and power in networks
Correlating centrality with social roles
Ranking nodes by importance
Centrality in directed vs undirected graphs
Case Study: Identifying key decision-makers in a corporate hierarchy

Module 4: Community Detection and Network Clustering

Introduction to modularity and clustering
Popular algorithms: Girvan-Newman, Louvain method
Detecting cohesive subgroups
Visualization of clusters
Understanding echo chambers in social systems
Case Study: Discovering online echo chambers in Twitter political discussions

Module 5: Tools and Software for Network Analysis

Introduction to Gephi and Pajek
Visualizing networks with NetworkX in Python
Importing and cleaning datasets
Customizing graph layouts and attributes
Interpreting results with dashboards and visuals
Case Study: Visualizing a health communication network using Gephi

Module 6: Multimodal and Dynamic Network Modeling

Modeling bipartite and multiplex networks
Temporal networks and time-series analysis
Visualization of evolving networks
Dynamics of information and virus spreading
Integrating qualitative data in dynamic networks
Case Study: Tracking NGO collaborations over 5 years

Module 7: Applications in Public Policy and Social Impact

Network approaches to policy evaluation
Stakeholder analysis using graph theory
Inter-agency collaboration analysis
Using networks for impact assessment
Policy diffusion through social networks
Case Study: Stakeholder mapping in a rural development program

Module 8: Ethical, Practical, and Research Design Considerations

Privacy and anonymity in network data
IRB and consent in SNA research
Handling missing or noisy data
Integrating graphs in mixed-methods design
Writing and publishing graph-based research
Case Study: Designing an ethical SNA research project on youth peer influence

Training Methodology

Instructor-led live sessions with real-time demonstrations
Hands-on activities using Gephi, NetworkX, and UCINET
Guided analysis of pre-collected social datasets
Group discussions and peer-to-peer learning
Application-based case study analysis per module
Final capstone mini-project with instructor feedback

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Network analysis graph theory social science applications

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000