Course Introduction
Social Network Analysis for Research and Policy Training Course provides a practical and research-oriented foundation for understanding how relationships, interactions, influence, information flows, and institutional connections shape social, economic, political, and policy outcomes. Participants learn to represent complex relationships as networks and apply analytical methods to identify patterns that may not be visible through conventional tabular datasets. The course connects social network theory with practical research and policy applications, enabling participants to investigate actors, relationships, communities, brokers, clusters, and pathways using structured network data.
The course introduces the core concepts of social network analysis, including nodes, edges, ties, direction, strength, density, degree, components, paths, centrality, cohesion, structural holes, and community structure. Participants learn how to translate research questions into network models and distinguish between individual-level attributes and relational information. Particular attention is given to interpreting network measures carefully, because a highly connected actor, a strategically positioned intermediary, and a member of a tightly connected community can play very different roles depending on the research question, network boundary, and type of relationship being analyzed.
Participants develop practical skills for preparing and analyzing network datasets using Python-based workflows and graph analytics tools. NetworkX provides extensive capabilities for graph construction, shortest-path analysis, centrality, and related network algorithms, including degree, eigenvector, closeness, and betweenness centrality. The training emphasizes reproducible workflows in which researchers can document how relationships were defined, transform source data into graph structures, calculate network measures, visualize results, and communicate findings transparently to technical and non-technical audiences.
A major focus of the course is applying social network analysis to research and policy questions. Participants explore how networks can be used to study collaboration among organizations, stakeholder relationships, policy communities, service-delivery networks, research partnerships, community structures, communication patterns, institutional coordination, and the diffusion of information or resources. The course also examines how network evidence can complement surveys, interviews, administrative records, qualitative research, and conventional statistical analysis, helping analysts develop more complete explanations of how relationships influence or constrain observed outcomes.
The course also introduces advanced graph analytics concepts relevant to larger and more complex networks. Participants examine community detection, similarity analysis, path finding, ranking, and graph-based analytical workflows using modern graph data science platforms. Neo4j Graph Data Science currently organizes its algorithms into areas including centrality, community detection, similarity, path finding, node embeddings, and machine learning. Community detection methods such as Leiden, Louvain, label propagation, connected components, k-core decomposition, and triangle counting can support investigations of network structure and group formation, while path-finding algorithms can help analyze connectivity and routes through complex relational systems.
By the end of Social Network Analysis for Research and Policy Training Course, participants will be able to design network studies, prepare relational datasets, construct and analyze graphs, select appropriate network measures, interpret structural patterns, investigate communities and pathways, and communicate evidence for research and policy purposes. The course places strong emphasis on methodological rigor, ethical considerations, appropriate interpretation, reproducibility, and avoiding unsupported causal claims from relational data. Participants finish with a practical analytical framework that can be adapted to academic research, policy evaluation, stakeholder analysis, development programs, institutional research, public administration, and evidence-based decision-making.
Duration
5 days
Who Should Attend
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Researchers and academic professionals conducting studies involving social relationships, collaboration, communication, organizations, or communities.
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Policy analysts who need to understand stakeholder relationships, institutional connections, influence structures, and policy networks.
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Government officials and public-sector analysts examining coordination, service delivery, institutional collaboration, and community relationships.
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Monitoring and evaluation professionals analyzing relationships among programs, organizations, beneficiaries, partners, and implementation actors.
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Social scientists seeking practical skills in quantitative and computational social network analysis.
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Economists and development researchers studying networks, market relationships, institutional linkages, or socioeconomic interactions.
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NGO and international development professionals investigating stakeholder ecosystems, community structures, and program implementation networks.
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Data analysts who want to extend conventional statistical analysis with graph-based and relational analytical methods.
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Public policy researchers working with stakeholder, legislative, institutional, organizational, or inter-agency relationship data.
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University lecturers and research supervisors seeking practical approaches for teaching or applying network analysis methodologies.
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Information and knowledge management professionals studying communication, collaboration, information-sharing, and organizational networks.
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Professionals responsible for evidence-based research, strategic planning, stakeholder mapping, or network-informed decision-making.
Course Objectives
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Develop a strong conceptual understanding of social network analysis, including nodes, edges, ties, network boundaries, relational data, and fundamental graph structures used in research.
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Translate substantive research and policy questions into appropriate network representations while distinguishing relational variables from conventional individual-level attributes.
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Prepare, clean, validate, and structure relational datasets so that they can be reliably transformed into directed, undirected, weighted, or multiplex network models.
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Calculate and interpret major network measures including degree, density, centrality, clustering, connectivity, path length, and other structural indicators for research applications.
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Use Python and NetworkX to construct graphs, explore network structures, calculate analytical measures, investigate paths, and develop reproducible network analysis workflows.
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Analyze network communities and subgroups to identify structural patterns, clusters, cohesive groups, and potential divisions within social or organizational systems.
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Apply graph-based techniques to stakeholder analysis, collaboration research, institutional mapping, policy networks, development programs, and other real-world research contexts.
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Evaluate network findings critically by considering sampling, missing relationships, network boundaries, measurement choices, uncertainty, ethics, and limitations of relational datasets.
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Communicate network findings effectively through appropriate visualizations, analytical summaries, research reports, policy briefs, and evidence-based presentations for diverse audiences.
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Develop an end-to-end social network analysis project that integrates data preparation, graph construction, network metrics, interpretation, visualization, and policy or research recommendations.
Comprehensive Course Outline
Module 1: Foundations of Social Network Analysis
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Introduction to social network analysis, relational thinking, network science, and the differences between network data and conventional rectangular research datasets.
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Core network concepts including nodes, edges, ties, direction, weight, reciprocity, affiliation, network boundaries, ego networks, and complete network structures.
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Understanding graph types such as directed, undirected, weighted, bipartite, temporal, multilayer, and multiplex networks and their relevance to research questions.
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Connecting substantive research questions to network models, analytical assumptions, measurement choices, and appropriate interpretations of observed relational structures.
Module 2: Network Research Design and Data Collection
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Designing social network research studies around clearly defined actors, relationships, populations, research questions, hypotheses, and analytical units.
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Identifying suitable sources of relational data including surveys, interviews, administrative records, organizational databases, collaboration records, communication data, and publicly available information.
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Constructing network boundaries and sampling strategies while assessing coverage, missing ties, non-response, duplicate relationships, and potential sources of network bias.
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Addressing research ethics, privacy, informed consent, confidentiality, sensitive relationship data, responsible disclosure, and safeguards for network-based research.
Module 3: Network Data Preparation and Graph Construction
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Cleaning relational datasets by resolving identifiers, standardizing relationship types, removing duplicate records, handling missing values, and validating node and edge attributes.
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Transforming tabular edge lists and node tables into graph structures suitable for computational social network analysis and reproducible research workflows.
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Building directed, undirected, weighted, and bipartite networks while selecting appropriate representations for different social, institutional, and policy relationships.
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Using Python and NetworkX to create graphs, inspect nodes and edges, calculate basic structural properties, and establish reusable analytical workflows.
Module 4: Descriptive Network Analysis and Structural Measures
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Calculating and interpreting network size, density, degree distributions, connected components, clustering, reciprocity, path lengths, and other descriptive structural measures.
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Understanding degree centrality and distinguishing incoming and outgoing connectivity in directed networks when analyzing relationships such as communication, referrals, or influence.
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Examining network cohesion, fragmentation, isolates, bridges, components, and structural patterns that can reveal important characteristics of social and institutional systems.
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Comparing network measures across groups, organizations, locations, or time periods while accounting for differences in network size, sampling, and data completeness.
Module 5: Centrality, Influence, and Brokerage
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Applying degree, betweenness, closeness, eigenvector, PageRank, and related centrality measures to investigate different dimensions of structural position.
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Interpreting betweenness and brokerage positions to investigate actors that connect otherwise separated groups or potentially facilitate the movement of information and resources.
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Understanding closeness centrality and its relationship to shortest-path distances, accessibility, and the potential speed of reaching other nodes within a network.
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Comparing centrality measures appropriately and avoiding simplistic interpretations of centrality as universal measures of power, importance, influence, or causality.
Module 6: Paths, Connectivity, and Network Diffusion
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Exploring shortest paths, reachability, connected components, network distance, and alternative routes for understanding how relationships connect actors and organizations.
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Applying weighted and unweighted path analysis to represent relationships where distance, cost, strength, time, or other edge attributes affect network connectivity.
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Investigating information, resource, behavior, or policy diffusion concepts and understanding how network structures may facilitate or constrain movement across connected populations.
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Using computational path-finding techniques to investigate relational routes and connectivity patterns in research, organizational, infrastructure, and policy networks.
Module 7: Community Detection and Network Clustering
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Understanding community detection as a method for identifying groups of nodes that exhibit stronger internal connectivity or meaningful structural organization.
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Applying community detection approaches such as Louvain, Leiden, label propagation, connected components, k-core analysis, and related techniques to research networks.
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Evaluating community structures using appropriate metrics and substantive knowledge rather than treating algorithmically generated groups as automatically meaningful social categories.
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Applying network clustering to stakeholder mapping, research collaboration, organizational analysis, community studies, policy ecosystems, and institutional coordination research.
Module 8: Network Visualization and Interpretation
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Designing network visualizations that communicate relationships, communities, central actors, bridges, clusters, and structural patterns without introducing misleading visual interpretations.
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Selecting layouts, node attributes, edge properties, labels, colors, sizes, filters, and annotations appropriate to the research question and target audience.
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Combining network diagrams with statistical summaries, tables, centrality measures, community assignments, and contextual metadata to strengthen analytical interpretation.
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Developing research figures and policy-oriented network maps that communicate complex relational findings clearly to technical researchers, decision-makers, and non-specialist stakeholders.
Module 9: Advanced Graph Analytics for Research and Policy
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Exploring graph similarity, ranking, node embeddings, link prediction, and other advanced approaches that extend conventional descriptive social network analysis.
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Introducing Neo4j Graph Data Science workflows for graph projections, algorithm execution, result analysis, and integration of graph analytics into research-oriented data workflows.
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Applying advanced graph algorithms to larger relational datasets while considering computational requirements, graph structure, algorithm assumptions, and interpretation of generated measures.
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Connecting graph analytics with research design, predictive analysis, stakeholder intelligence, policy research, organizational studies, and evidence-based program analysis.
Module 10: Research, Policy Applications, and Capstone Project
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Developing an end-to-end social network analysis project from research question and data preparation through network construction, analytical measurement, visualization, interpretation, and reporting.
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Applying network analysis to practical policy contexts such as stakeholder coordination, service-delivery networks, institutional collaboration, development partnerships, community engagement, and research ecosystems.
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Evaluating findings critically by distinguishing descriptive network patterns from causal explanations and documenting methodological assumptions, limitations, missing data, and ethical considerations.
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Presenting a final network research or policy analysis project with defensible findings, clear visualizations, reproducible analytical steps, and actionable evidence appropriate for the intended audience.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
