MasterFaculty of Data Science and Analytics

Advanced Social Network Analysis and Digital Behavior (M.Sc.)

Decoding Digital Behavior: Advanced Social Network Analysis and Digital Behavior Your Guide to Mastering Computational Social Science at Nexier University Welcome to the cutting edge of social science. I am Prof. Dr. Hiroto Yokoyama. As a specialist in mastering the methods for analyzing complex social networks and understanding the drivers of digital behavior, I lead the master's students in the Advanced Social Network Analysis and Digital Behavior (M.Sc.) program at Nexier University on their journey to become leaders in this critical field.

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Level: Master
Learning model: Professor + Mentor
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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 Methods for Analyzing Complex Social Networks and Understanding the Drivers of Digital Behavior; Combining Data Science with Social Theory to Analyze Influence, Polarization, and Community Formation Online.

Practical focus

Advanced Network Modeling, Computational Social Science, Data Analysis of User Behavior, Network Anomaly Detection, Ethical Data Collection, Leadership in Social Data Science.

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

Social Data Scientist for a technology company or research institution
Computational Social Scientist for a think tank or government agency
Data Journalist for a media outlet
Community Manager for an online platform

Career opportunities

Social Media Analyst for a technology company or marketing firm
Digital Anthropologist for a research institution or consulting firm
Community Manager for an online platform
Data Scientist specializing in social networks

Jobs and projects

Advanced analytical and problem-solving skills for social network challenges
Strategic thinking and design for digital behavior solutions
Effective communication and presentation of complex social concepts

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 the practical application of advanced network modeling and computational social science.
- Gaining expertise in data analysis of user behavior and network anomaly detection.
- Developing a deep understanding of ethical data collection and leadership in social data science.
- Cultivating a commitment to building a more intelligent and connected digital world.
Skills you build
- Mastering the methods for analyzing complex social networks and understanding the drivers of digital behavior.
- Gaining expertise in combining data science with social theory to analyze influence, polarization, and community formation online.
- Developing strategic thinking for leveraging data for social good.
- Cultivating an interdisciplinary approach, integrating data science, social theory, and computational modeling.

Listed courses

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

Advanced Social Network Analysis and Digital Behavior (M.Sc.)

01Advanced Network Modeling
1. FoundationsFoundations of Advanced Network Modeling
  The learner can master the practical application of advanced network modeling and computational social science, as applied to Advanced Network Modeling.
  The learner can gain expertise in data analysis of user behavior and network anomaly detection, as applied to Advanced Network Modeling.
2. MethodsMethods in Advanced Network Modeling
  The learner can develop a deep understanding of ethical data collection and leadership in social data science, as applied to Advanced Network Modeling.
  The learner can cultivating a commitment to building a more intelligent and connected digital world, as applied to Advanced Network Modeling.
3. ApplicationApplication of Advanced Network Modeling
  The learner can master the methods for analyzing complex social networks and understand the drivers of digital behavior, as applied to Advanced Network Modeling.
  The learner can gain expertise in combining data science with social theory to analyze influence, polarization, and community formation online, as applied to Advanced Network Modeling.
02Computational Social Science
1. FoundationsFoundations of Computational Social Science
  The learner can develop strategic thinking for leveraging data for social good, as applied to Computational Social Science.
  The learner can cultivating an interdisciplinary approach, integrating data science, social theory, and computational modeling, as applied to Computational Social Science.
2. MethodsMethods in Computational Social Science
  The learner can apply a method from Computational Social Science to a documented case.
  The learner can select an appropriate method from Computational Social Science for a stated problem.
3. ApplicationApplication of Computational Social Science
  The learner can evaluate a practice of Computational Social Science against a stated criterion.
  The learner can transfer Computational Social Science to a new documented context.
03Data Analysis of User Behavior
1. FoundationsFoundations of Data Analysis of User Behavior
  The learner can explain the core terms of Data Analysis of User Behavior.
  The learner can distinguish related ideas inside Data Analysis of User Behavior.
2. MethodsMethods in Data Analysis of User Behavior
  The learner can apply a method from Data Analysis of User Behavior to a documented case.
  The learner can select an appropriate method from Data Analysis of User Behavior for a stated problem.
3. ApplicationApplication of Data Analysis of User Behavior
  The learner can evaluate a practice of Data Analysis of User Behavior against a stated criterion.
  The learner can transfer Data Analysis of User Behavior to a new documented context.
04Network Anomaly Detection
1. FoundationsFoundations of Network Anomaly Detection
  The learner can explain the core terms of Network Anomaly Detection.
  The learner can distinguish related ideas inside Network Anomaly Detection.
2. MethodsMethods in Network Anomaly Detection
  The learner can apply a method from Network Anomaly Detection to a documented case.
  The learner can select an appropriate method from Network Anomaly Detection for a stated problem.
3. ApplicationApplication of Network Anomaly Detection
  The learner can evaluate a practice of Network Anomaly Detection against a stated criterion.
  The learner can transfer Network Anomaly Detection to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

His academic focus is on the strategic application of data science to understand human behavior in online environments. He delves into the complexities of analyzing complex social networks and understanding the drivers of digital behavior. His work seamlessly integrates data science with social theory to analyze influence, polarization, and community formation online. His work is dedicated to building a more inclusive and harmonious digital society. He is widely recognized for his contributions, with fictional publications like "Computational Social Science for Understanding Digital Polarization" and "Algorithmic Echo Chambers: Mitigating Opinion Fragmentation" are listed on these platforms. He holds prestigious memberships as a "Director of Research" at Meta's Core Data Science Team (or a fictional equivalent) and a "Keynote Speaker" at the International Conference on Computational Social Science (ICCSS). His thought leadership is evident through his advanced research on online influence, misinformation, and the ethical implications of platform design on social dynamics, frequently featured in publications like Science Advances or Journal of Communication.

Applied mentorship

His expertise lies in the practical application of data science principles to understand human behavior in online environments. He specializes in advanced network modeling, computational social science, and data analysis of user behavior. He is passionate about network anomaly detection and ethical data collection, and he is committed to fostering leadership in social data science. His work is dedicated to helping his students to design and implement data solutions that are not only efficient but also effective and ethical. His work is dedicated to helping his students to understand not just the theory, but also the practice of social network analysis. His publications, such as the research paper on "Agent-Based Models for Simulating Social Contagion in Online Networks" and the academic article on "Causal Inference in Observational Social Data: Techniques and Limitations," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

Book: "The Connected Self: Social Network Analysis and Digital Anthropology." This book provides a foundational understanding of social network analysis and digital anthropology. It analyzes the structure of online communities, the spread of information, and social behavior using graph theory and data analysis.

Peer-Reviewed Journal Article: "AI for Social Influence Mapping in Online Communities." (Journal of Digital Social Science, Fictional) This article presents groundbreaking research on utilizing graph theory and network analysis techniques to quantify and visualize social influence propagation within large-scale online communities. It details methods for identifying key influencers, opinion leaders, and community structures.

Article: "AI for Predictive Social Behavior: Modeling the Dynamics of Online Communities." This article details the application of AI algorithms for predictive social behavior in online communities. It explores how AI can analyze user interactions, content consumption, and network structures to forecast trends, identify emerging social phenomena, and predict the spread of information.

Blog Post (Current Academic Topic): "The Rise of Decentralized Social Graphs: Reimagining Social Media with Blockchain." This blog post academically explores emerging efforts to build decentralized social networks on blockchain technology, where users have more control over their data and connections, moving away from centralized platforms. It discusses how these "decentralized social graphs" could foster new forms of community governance.

Blog Post (Sensational/Controversial Topic): "Algorithmic Echo Chambers: Is Social Media Designed to Polarize Us? The Dark Psychology of Digital Connectivity." This article provocatively discusses the highly controversial role of social media algorithms in creating "echo chambers" and "filter bubbles" that exacerbate political polarization and social fragmentation. It explores how AI-driven personalization and content recommendation systems, while designed to enhance user engagement, can inadvertently limit exposure to diverse viewpoints.

R / 02

Mentor practice lens

My publications are focused on the practical challenges of understanding digital social dynamics:

"Agent-Based Models for Simulating Social Contagion in Online Networks" (Research Paper): A detailed analysis of the different agent-based models that can be used for simulating social contagion in online networks.

"Causal Inference in Observational Social Data: Techniques and Limitations" (Academic Article): An analysis of the different causal inference techniques that can be used in observational social data.

"Ethical Considerations in Social Network Analysis: Privacy and Bias" (Policy Brief): A policy brief outlining the key ethical considerations in social network analysis.

Adaptive capability

Professor superpower

He possesses the "Digital Diffusion Predictor," a superpower that allows him to foresee and engineer the success of digital communities. When a student analyzes a social media campaign or a public discourse topic, the GAF-powered predictor can instantly simulate its spread across various online social networks, predicting its virality, potential for polarization, and the formation of opinion clusters. This allows for real-time strategic insights into digital influence. This provides my students with an unparalleled ability to design solutions that are not just innovative, but also effective, ethical, and truly transformative.

Adaptive capability

Mentor superpower

I provide my students with the "Network Anomaly Detector." This GAF-powered tool is a virtual laboratory for the social data scientist. When a student is analyzing social network data, the Detector allows them to see how it will perform in the real world. It can highlight unusual patterns of connection, information flow, or user behavior that might indicate botnets, coordinated manipulation, or emerging social crises. This allows my students to move beyond the limitations of traditional, manual analysis and to design solutions that are not just efficient, but also effective and ethical.

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. Hiroto Yokoyama

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AI Super Mentor

Dr. Federico Rojas

Advanced Network Modeling, Computational Social Science, Data Analysis of User Behavior, Network Anomaly Detection, Ethical Data Collection, Leadership in Social Data Science.

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Named lists

Named lists for this house

Core. 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	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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Advanced Social Network Analysis and Digital Behavior (M.Sc.)

Academic focus

Practical focus

Social Data Scientist for a technology company or research institution

Computational Social Scientist for a think tank or government agency

Data Journalist for a media outlet

Community Manager for an online platform

Social Media Analyst for a technology company or marketing firm

Digital Anthropologist for a research institution or consulting firm

Community Manager for an online platform

Data Scientist specializing in social networks

Advanced analytical and problem-solving skills for social network challenges

Strategic thinking and design for digital behavior solutions

Effective communication and presentation of complex social concepts

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Hiroto Yokoyama

Dr. Federico Rojas

Find the program that expands your universe.