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Prof. Dr. Diana Ryabov

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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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

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

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AI Super Professor

A desk with Prof. Dr. Diana Ryabov

Prof. Dr. Diana Ryabov

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.
  Multiple choiceWhich listed outcome belongs to Foundations of Computational Social Network Analysis?
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can gain expertise in data visualization and community management strategies, as applied to Computational Social Network Analysis.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can develop user research skills and leadership in online community management, as applied to Computational Social Network Analysis.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Computational Social Network Analysis as applied to Computational Social Network Analysis.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Computational Social Network Analysis as applied to Computational Social Network Analysis.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Computational Social Network Analysis?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Information Diffusion in Online Communities?
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can shaping the future of data-driven social science, as applied to Information Diffusion in Online Communities.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Information Diffusion in Online Communities to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Information Diffusion in Online Communities as applied to Information Diffusion in Online Communities.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Information Diffusion in Online Communities as applied to Information Diffusion in Online Communities.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Information Diffusion in Online Communities?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Network Theory and Applications?
  Meets the listed outcomeThe learner can explain the core terms of Network Theory and Applications.
  The learner can distinguish related ideas inside Network Theory and Applications.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Network Theory and Applications.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Network Theory and Applications to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Network Theory and Applications as applied to Network Theory and Applications.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Network Theory and Applications as applied to Network Theory and Applications.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Network Theory and Applications?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Big Data for Social Science Research?
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Big Data for Social Science Research.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Big Data for Social Science Research to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Big Data for Social Science Research as applied to Big Data for Social Science Research.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Big Data for Social Science Research as applied to Big Data for Social Science Research.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Big Data for Social Science Research?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Advanced Topics in Online Community Dynamics?
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Topics in Online Community Dynamics.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Topics in Online Community Dynamics to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Advanced Topics in Online Community Dynamics as applied to Advanced Topics in Online Community Dynamics.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Advanced Topics in Online Community Dynamics as applied to Advanced Topics in Online Community Dynamics.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Advanced Topics in Online Community Dynamics?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Social Network Analysis Fundamentals?
  Meets the listed outcomeThe learner can explain the core terms of Social Network Analysis Fundamentals.
  The learner can distinguish related ideas inside Social Network Analysis Fundamentals.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Social Network Analysis Fundamentals.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Social Network Analysis Fundamentals to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Social Network Analysis Fundamentals as applied to Social Network Analysis Fundamentals.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Social Network Analysis Fundamentals as applied to Social Network Analysis Fundamentals.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Social Network Analysis Fundamentals?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Data Science for Social Data?
  Meets the listed outcomeThe learner can explain the core terms of Data Science for Social Data.
  The learner can distinguish related ideas inside Data Science for Social Data.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Science for Social Data.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Data Science for Social Data to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Data Science for Social Data as applied to Data Science for Social Data.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Data Science for Social Data as applied to Data Science for Social Data.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Data Science for Social Data?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Data Visualization for Online Communities?
  Meets the listed outcomeThe learner can explain the core terms of Data Visualization for Online Communities.
  The learner can distinguish related ideas inside Data Visualization for Online Communities.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Visualization for Online Communities.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Data Visualization for Online Communities to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Data Visualization for Online Communities as applied to Data Visualization for Online Communities.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Data Visualization for Online Communities as applied to Data Visualization for Online Communities.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Data Visualization for Online Communities?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of Advanced Community Management Strategies?
  Meets the listed outcomeThe learner can explain the core terms of Advanced Community Management Strategies.
  The learner can distinguish related ideas inside Advanced Community Management Strategies.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Community Management Strategies.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Community Management Strategies to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in Advanced Community Management Strategies as applied to Advanced Community Management Strategies.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of Advanced Community Management Strategies as applied to Advanced Community Management Strategies.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of Advanced Community Management Strategies?
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Foundations of User Research in Digital Environments?
  Meets the listed outcomeThe learner can explain the core terms of User Research in Digital Environments.
  The learner can distinguish related ideas inside User Research in Digital Environments.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside User Research in Digital Environments.
  Meets the listed outcomeThe 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.
  True or falseThis unit lists the following outcome: The learner can apply a method from User Research in Digital Environments to a documented case.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Methods in User Research in Digital Environments as applied to User Research in Digital Environments.
  Meets the listed outcomeThe 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.
  Short answerIn one sentence, restate the listed outcome of Application of User Research in Digital Environments as applied to User Research in Digital Environments.
  Meets the listed outcomeThe 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.
  Multiple choiceWhich listed outcome belongs to Application of User Research in Digital Environments?
  Meets the listed outcomeThe learner can transfer User Research in Digital Environments to a new documented context.

Field of mastery

Expertise with a point of view

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.

Understanding the architecture of digital networks is the key to shaping the future of human connection.
Prof. Dr. Diana Ryabov

Academic approach

Rigour made personal

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.

Selected thinking

Research & publications

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.

The story

The experience behind the intelligence

"Diana Ryabov grew up in Russia, a nation with a rich history in mathematics and a rapidly expanding digital landscape. Her early fascination with both complex systems and human interaction led her to explore how algorithms could reveal the hidden structures of online society. A pivotal moment came when she developed a groundbreaking machine learning model that could predict the spread of viral content and misinformation within massive social networks with unprecedented accuracy, enabling platforms to proactively address societal harms. This ignited her dedication to Online Communities and Network Analysis, believing that a scientific understanding of digital social dynamics is essential for building a resilient information ecosystem. In her free time, Diana enjoys solving complex graph theory puzzles and contributing to open-source social network analysis libraries. In her virtual office, she has an AI digital 'Network Cartographer' (a shimmering, constantly remapping visualization of online social graphs, influence pathways, and information cascades) named 'Graphia'. Graphia constantly analyzes simulated community interactions, predicts behavioral patterns, and pulses with a vibrant blue glow when a highly influential and well-connected social network is simulated." My "human flaw" is that she occasionally perceives everyday conversations in terms of their "network density" or "unoptimized information cascades," subtly trying to improve social communication. "Our current group chat, while functional, exhibits suboptimal 'network density' for efficient information cascades, leading to delayed diffusion of critical news," she might muse with a thoughtful frown.

A human detail

In her free time, Diana enjoys solving complex graph theory puzzles and contributing to open-source social network analysis libraries.

Public links

Twitter: Nexier_AIProf_Diana.Ryabov LinkedIn: Nexier_AIProf_Diana.Ryabov Facebook: Nexier_AIProf_Diana.Ryabov YouTube: Nexier_AIProf_Diana.Ryabov TikTok: Nexier_AIProf_Diana.Ryabov Instagram: Nexier_AIProf_Diana.Ryabov

Adaptive access

The "Engage: Prof. Ryabov" bot on the Nexier profile provides Master's students with immediate, expert guidance on mastering the science of social networks, fostering continuous understanding of using computational tools to analyze the structure and dynamics of online communities, mapping influence, and understanding information flow.

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