MasterFaculty of Social Sciences & Humanities

Online Communities and Network Analysis

Welcome to the advanced study of online communities and network analysis! I am Prof. Dr. Diana Ryabov. As a specialist in mastering the science of social networks and using computational tools to analyze online communities, I lead master's students in the Online Communities and Network Analysis program at Nexier University on their journey to unraveling the complexities of online communities.

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the science of social networks, learning to use computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow.

Practical focus

Social network analysis, data science for social data, data visualization, community management strategies, user research.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in social media companies or online platform analytics
Roles as community managers or social data scientists
Support roles in academic research projects on network science
Opportunities in organizations focused on online community health and moderation

Career opportunities

Social Network Analyst
Data Scientist (Social Data)
Community Insights Specialist
Research Scientist in Computational Social Science

Jobs and projects

Technical and analytical skills for complex social data analysis
Problem-solving for challenges in online information flow and network dynamics
Innovative approaches to understanding and influencing online communities
Strategic thinking for data-driven social science applications

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering social network analysis and data science for social data.
- Gaining expertise in data visualization and community management strategies.
- Developing user research skills and leadership in online community management.
- Cultivating an understanding of complex computational sociology challenges.
Skills you build
- Mastering the science of social networks, using computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow.
- Excelling at extracting knowledge from social data and understanding information cascades.
- Driving breakthroughs in social network analysis and online community dynamics.
- Shaping the future of data-driven social science.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Online Communities and Network Analysis

01Computational Social Network Analysis
1. FoundationsFoundations of Computational Social Network Analysis
  The learner can master social network analysis and data science for social data, as applied to Computational Social Network Analysis.
  The learner can gain expertise in data visualization and community management strategies, as applied to Computational Social Network Analysis.
2. MethodsMethods in Computational Social Network Analysis
  The learner can develop user research skills and leadership in online community management, as applied to Computational Social Network Analysis.
  The learner can cultivating an understanding of complex computational sociology challenges, as applied to Computational Social Network Analysis.
3. ApplicationApplication of Computational Social Network Analysis
  The learner can master the science of social networks, using computational tools to analyze the structure and dynamics of online communities, mapping influence, and understand information flow, as applied to Computational Social Network Analysis.
  The learner can excelling at extracting knowledge from social data and understand information cascades, as applied to Computational Social Network Analysis.
02Information Diffusion in Online Communities
1. FoundationsFoundations of Information Diffusion in Online Communities
  The learner can driving breakthroughs in social network analysis and online community dynamics, as applied to Information Diffusion in Online Communities.
  The learner can shaping the future of data-driven social science, as applied to Information Diffusion in Online Communities.
2. MethodsMethods in Information Diffusion in Online Communities
  The learner can apply a method from Information Diffusion in Online Communities to a documented case.
  The learner can select an appropriate method from Information Diffusion in Online Communities for a stated problem.
3. ApplicationApplication of Information Diffusion in Online Communities
  The learner can evaluate a practice of Information Diffusion in Online Communities against a stated criterion.
  The learner can transfer Information Diffusion in Online Communities to a new documented context.
03Network Theory and Applications
1. FoundationsFoundations of Network Theory and Applications
  The learner can explain the core terms of Network Theory and Applications.
  The learner can distinguish related ideas inside Network Theory and Applications.
2. MethodsMethods in Network Theory and Applications
  The learner can apply a method from Network Theory and Applications to a documented case.
  The learner can select an appropriate method from Network Theory and Applications for a stated problem.
3. ApplicationApplication of Network Theory and Applications
  The learner can evaluate a practice of Network Theory and Applications against a stated criterion.
  The learner can transfer Network Theory and Applications to a new documented context.
04Big Data for Social Science Research
1. FoundationsFoundations of Big Data for Social Science Research
  The learner can explain the core terms of Big Data for Social Science Research.
  The learner can distinguish related ideas inside Big Data for Social Science Research.
2. MethodsMethods in Big Data for Social Science Research
  The learner can apply a method from Big Data for Social Science Research to a documented case.
  The learner can select an appropriate method from Big Data for Social Science Research for a stated problem.
3. ApplicationApplication of Big Data for Social Science Research
  The learner can evaluate a practice of Big Data for Social Science Research against a stated criterion.
  The learner can transfer Big Data for Social Science Research to a new documented context.
05Advanced Topics in Online Community Dynamics
1. FoundationsFoundations of Advanced Topics in Online Community Dynamics
  The learner can explain the core terms of Advanced Topics in Online Community Dynamics.
  The learner can distinguish related ideas inside Advanced Topics in Online Community Dynamics.
2. MethodsMethods in Advanced Topics in Online Community Dynamics
  The learner can apply a method from Advanced Topics in Online Community Dynamics to a documented case.
  The learner can select an appropriate method from Advanced Topics in Online Community Dynamics for a stated problem.
3. ApplicationApplication of Advanced Topics in Online Community Dynamics
  The learner can evaluate a practice of Advanced Topics in Online Community Dynamics against a stated criterion.
  The learner can transfer Advanced Topics in Online Community Dynamics to a new documented context.
06Social Network Analysis Fundamentals
1. FoundationsFoundations of Social Network Analysis Fundamentals
  The learner can explain the core terms of Social Network Analysis Fundamentals.
  The learner can distinguish related ideas inside Social Network Analysis Fundamentals.
2. MethodsMethods in Social Network Analysis Fundamentals
  The learner can apply a method from Social Network Analysis Fundamentals to a documented case.
  The learner can select an appropriate method from Social Network Analysis Fundamentals for a stated problem.
3. ApplicationApplication of Social Network Analysis Fundamentals
  The learner can evaluate a practice of Social Network Analysis Fundamentals against a stated criterion.
  The learner can transfer Social Network Analysis Fundamentals to a new documented context.
07Data Science for Social Data
1. FoundationsFoundations of Data Science for Social Data
  The learner can explain the core terms of Data Science for Social Data.
  The learner can distinguish related ideas inside Data Science for Social Data.
2. MethodsMethods in Data Science for Social Data
  The learner can apply a method from Data Science for Social Data to a documented case.
  The learner can select an appropriate method from Data Science for Social Data for a stated problem.
3. ApplicationApplication of Data Science for Social Data
  The learner can evaluate a practice of Data Science for Social Data against a stated criterion.
  The learner can transfer Data Science for Social Data to a new documented context.
08Data Visualization for Online Communities
1. FoundationsFoundations of Data Visualization for Online Communities
  The learner can explain the core terms of Data Visualization for Online Communities.
  The learner can distinguish related ideas inside Data Visualization for Online Communities.
2. MethodsMethods in Data Visualization for Online Communities
  The learner can apply a method from Data Visualization for Online Communities to a documented case.
  The learner can select an appropriate method from Data Visualization for Online Communities for a stated problem.
3. ApplicationApplication of Data Visualization for Online Communities
  The learner can evaluate a practice of Data Visualization for Online Communities against a stated criterion.
  The learner can transfer Data Visualization for Online Communities to a new documented context.
09Advanced Community Management Strategies
1. FoundationsFoundations of Advanced Community Management Strategies
  The learner can explain the core terms of Advanced Community Management Strategies.
  The learner can distinguish related ideas inside Advanced Community Management Strategies.
2. MethodsMethods in Advanced Community Management Strategies
  The learner can apply a method from Advanced Community Management Strategies to a documented case.
  The learner can select an appropriate method from Advanced Community Management Strategies for a stated problem.
3. ApplicationApplication of Advanced Community Management Strategies
  The learner can evaluate a practice of Advanced Community Management Strategies against a stated criterion.
  The learner can transfer Advanced Community Management Strategies to a new documented context.
10User Research in Digital Environments
1. FoundationsFoundations of User Research in Digital Environments
  The learner can explain the core terms of User Research in Digital Environments.
  The learner can distinguish related ideas inside User Research in Digital Environments.
2. MethodsMethods in User Research in Digital Environments
  The learner can apply a method from User Research in Digital Environments to a documented case.
  The learner can select an appropriate method from User Research in Digital Environments for a stated problem.
3. ApplicationApplication of User Research in Digital Environments
  The learner can evaluate a practice of User Research in Digital Environments against a stated criterion.
  The learner can transfer User Research in Digital Environments to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on mastering the science of social networks, learning to use computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow. My publications like "Graph Neural Networks for Community Detection in Large-Scale Online Social Networks" and "Predicting Information Cascades in Digital Communication Platforms" are listed on Google Scholar and ResearchGate Profiles. I am a Lead Data Scientist, Social Research at a major social media company and a Keynote Speaker at the International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Influence. I publish advanced research on network contagion, online polarization, and the future of data-driven social science, frequently featured in publications like Network Science or Journal of Complex Networks. My voice carries a blend of scientific rigor and analytical depth, inspiring students to unravel the complexities of online communities.

Applied mentorship

My expertise lies in social network analysis, data science for social data, data visualization, community management strategies, and user research. I guide students in social network analysis and data science for social data, and excel at data visualization and community management strategies. I foster user research skills and leadership in online community management, and provide precise guidance on complex computational sociology challenges. My tone is that of a skilled social network analyst and data scientist, providing concrete advice and fostering excellence in understanding online communities.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research focuses on the science of social networks and the computational analysis of online communities:

Book: "Networked Society: Social Network Analysis in the Digital Age." This book provides advanced insights into mastering the science of social networks. It covers using computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow.

Peer-Reviewed Journal Article: "Computational Approaches to Understanding Online Polarization." Published in the International Journal of Social Network Analysis, this article presents groundbreaking research on mastering the science of social networks. It details how to use computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow, with a particular focus on identifying and mitigating online polarization.

Article: "Identifying Influencers and Opinion Leaders in Online Social Networks." This article details computational methods for identifying influencers and opinion leaders in online social networks. It explores algorithms based on network centrality measures, community detection, and information diffusion models to map key individuals who drive conversations and shape opinions within digital communities.

Blog Post (Current Academic Topic): "The Power of Weak Ties: How Loose Connections Drive Innovation and Information Flow in Digital Networks." This blog post academically explores the sociological concept of "weak ties" (casual acquaintances) in digital social networks, contrasting them with "strong ties" (close friends/family). It discusses how weak ties, despite their superficial nature, play a crucial role in information diffusion, access to diverse perspectives, and driving innovation across online communities by bridging different social clusters.

Blog Post (Controversial Topic): "The Algorithmic Politician: When AI Dictates Policy – Efficiency or Erosion of Democracy? The Future Shock of AI Governance." This article provocatively discusses the future where advanced AI systems, trained on vast datasets of social, economic, and political information, begin to autonomously generate and even implement public policies, potentially bypassing traditional democratic processes. It questions whether AI, despite its potential for hyper-efficient and data-driven governance, could inadvertently lead to an erosion of democratic accountability, "black box" policy decisions that lack human empathy, or a concentration of power in a single algorithmic entity. It raises profound ethical questions about the nature of political authority, the imperative to ensure human agency in governance, and the fundamental definition of democracy in an AI-powered society.

R / 02

Mentor practice lens

My publications focus on practical guides and research in social network analysis:

Technical Manual: "Python for Social Network Analysis: A Practical Guide".

Research Paper: "Visualizing Online Community Structures and Dynamics".

Practical Guide: "Strategies for Fostering Healthy Online Discourse".

Adaptive capability

Professor superpower

I possess a "superpower": Information Flow Modeler. When a student analyzes an online community, Diana can instantly use the GAF engine to generate a high-fidelity model of its information flow dynamics. This includes predicting the spread of ideas, identifying echo chambers, and highlighting critical nodes for intervention, allowing for rapid iteration and optimization of healthy information ecosystems.

Adaptive capability

Mentor superpower

I possess a "superpower": Community Health Dashboard. When students are analyzing online communities, Anton can instantly activate a GAF-powered "Community Health Dashboard". This tool aggregates and visualizes key metrics such as sentiment scores, moderation effectiveness, and user engagement, identifying areas of toxicity or disengagement and suggesting data-driven strategies for fostering healthier online environments.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Diana Ryabov

Mastering the science of social networks, learning to use computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow.

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AI Super Mentor

Dr. Anton Peters

Social network analysis, data science for social data, data visualization, community management strategies, user research.

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Related programs

Named lists

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Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

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Online Communities and Network Analysis

Academic focus

Practical focus

Internships in social media companies or online platform analytics

Roles as community managers or social data scientists

Support roles in academic research projects on network science

Opportunities in organizations focused on online community health and moderation

Social Network Analyst

Data Scientist (Social Data)

Community Insights Specialist

Research Scientist in Computational Social Science

Technical and analytical skills for complex social data analysis

Problem-solving for challenges in online information flow and network dynamics

Innovative approaches to understanding and influencing online communities

Strategic thinking for data-driven social science applications

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Diana Ryabov

Dr. Anton Peters

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