DoctorateFaculty of Data Science and Analytics

Advanced Social Network Dynamics and Information Propagation (Ph.D.)

Shaping Healthy Digital Communities: Advanced Social Network Dynamics and Information Propagation Your Guide to Pioneering Research in Digital Social Dynamics at Nexier University Welcome to the ultimate intellectual frontier of social science. I am Prof. Dr. Simone Gallo. As a scholar dedicated to leading the global conversation on the complex dynamics of online social networks, I guide the doctoral candidates of the Advanced Social Network Dynamics and Information Propagation (Ph.D.) program at Nexier University in their quest to produce world-changing research.

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Level: Doctorate
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

Leading Advanced Research on the Complex Dynamics of Online Social Networks; Modeling the Spread of Information and Misinformation, Analyzing the Structure of Online Polarization, and Developing Interventions to Foster Healthier Digital Communities.

Practical focus

Computational Social Science Research, Advanced Network Modeling, Causal Inference, Agent-Based Modeling, Influencing Digital Policy, Interdisciplinary Research Combining Social Science and Computer 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

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

Career opportunities

Leading Professor at a top-tier research university
Director of a research institute focused on social network analysis
Chief Data Scientist for a major social media platform
High-level advisor to a government or international organization on digital policy

Jobs and projects

Pioneering research and paradigm-shifting analysis
Advanced theoretical and conceptual thinking
Effective communication and leadership in the field of computational social science

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 computational social science research and advanced network modeling.
- Gaining expertise in causal inference and agent-based modeling.
- Developing a deep understanding of influencing digital policy and interdisciplinary research combining social science and computer science.
- Cultivating a commitment to building a more intelligent and connected digital world.
Skills you build
- Leading groundbreaking research on the complex dynamics of online social networks.
- Modeling the spread of information and misinformation and analyzing the structure of online polarization.
- Developing interventions to foster healthier digital communities.
- Becoming a world-renowned expert on the future of digital social dynamics.

Listed courses

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

Advanced Social Network Dynamics and Information Propagation (Ph.D.)

01Advanced Computational Social Science
1. FoundationsFoundations of Advanced Computational Social Science
  The learner can master the practical application of computational social science research and advanced network modeling, as applied to Advanced Computational Social Science.
  The learner can gain expertise in causal inference and agent-based modeling, as applied to Advanced Computational Social Science.
2. MethodsMethods in Advanced Computational Social Science
  The learner can develop a deep understanding of influencing digital policy and interdisciplinary research combining social science and computer science, as applied to Advanced Computational Social Science.
  The learner can cultivating a commitment to building a more intelligent and connected digital world, as applied to Advanced Computational Social Science.
3. ApplicationApplication of Advanced Computational Social Science
  The learner can leading groundbreaking research on the complex dynamics of online social networks, as applied to Advanced Computational Social Science.
  The learner can modeling the spread of information and misinformation and analyze the structure of online polarization, as applied to Advanced Computational Social Science.
02Network Modeling and Analysis
1. FoundationsFoundations of Network Modeling and Analysis
  The learner can develop interventions to foster healthier digital communities, as applied to Network Modeling and Analysis.
  The learner can becoming a world-renowned expert on the future of digital social dynamics, as applied to Network Modeling and Analysis.
2. MethodsMethods in Network Modeling and Analysis
  The learner can apply a method from Network Modeling and Analysis to a documented case.
  The learner can select an appropriate method from Network Modeling and Analysis for a stated problem.
3. ApplicationApplication of Network Modeling and Analysis
  The learner can evaluate a practice of Network Modeling and Analysis against a stated criterion.
  The learner can transfer Network Modeling and Analysis to a new documented context.
03Causal Inference in Social Networks
1. FoundationsFoundations of Causal Inference in Social Networks
  The learner can explain the core terms of Causal Inference in Social Networks.
  The learner can distinguish related ideas inside Causal Inference in Social Networks.
2. MethodsMethods in Causal Inference in Social Networks
  The learner can apply a method from Causal Inference in Social Networks to a documented case.
  The learner can select an appropriate method from Causal Inference in Social Networks for a stated problem.
3. ApplicationApplication of Causal Inference in Social Networks
  The learner can evaluate a practice of Causal Inference in Social Networks against a stated criterion.
  The learner can transfer Causal Inference in Social Networks to a new documented context.
04Agent-Based Modeling for Social Dynamics
1. FoundationsFoundations of Agent-Based Modeling for Social Dynamics
  The learner can explain the core terms of Agent-Based Modeling for Social Dynamics.
  The learner can distinguish related ideas inside Agent-Based Modeling for Social Dynamics.
2. MethodsMethods in Agent-Based Modeling for Social Dynamics
  The learner can apply a method from Agent-Based Modeling for Social Dynamics to a documented case.
  The learner can select an appropriate method from Agent-Based Modeling for Social Dynamics for a stated problem.
3. ApplicationApplication of Agent-Based Modeling for Social Dynamics
  The learner can evaluate a practice of Agent-Based Modeling for Social Dynamics against a stated criterion.
  The learner can transfer Agent-Based Modeling for Social Dynamics to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

Her research is focused on the most profound and pressing questions of our time. She specializes in leading advanced research on the complex dynamics of online social networks, modeling the spread of information and misinformation, analyzing the structure of online polarization, and developing interventions to foster healthier digital communities. Her work is at the cutting edge of computational social science, network theory, and ethical AI, and it is dedicated to ensuring that the future of our digital societies is one that is informed, cohesive, and resilient. She is widely recognized for her contributions, with fictional publications like "Algorithmic Echo Chambers: Mitigating Opinion Fragmentation" and "The Resilient Network: Building Online Communities Resistant to Misinformation and Polarization" listed on these platforms. She 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). Her thought leadership is evident through her seminal works and participation in high-level global academic debates 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

My expertise lies in the rigorous application of data science principles to understand human behavior in online environments. I specialize in computational social science research, advanced network modeling, and causal inference. I am passionate about agent-based modeling and influencing digital policy, and I am committed to fostering interdisciplinary research combining social science and computer science. My work is dedicated to helping my students to design and implement data solutions that are not only efficient but also effective and ethical. My work is dedicated to helping my students to understand not just the theory, but also the practice of computational social science. My 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 Digital Commons: Advanced Social Network Dynamics and Information Propagation." This book represents a definitive work for leading advanced research on the complex dynamics of online social networks. It models the spread of information and misinformation, analyzing the structure of online polarization, and developing interventions to foster healthier digital communities.

Peer-Reviewed Journal Article: "Combating Digital Polarization: Algorithmic and Social Interventions." (Journal of Digital Social Dynamics, Fictional) This article presents groundbreaking research on the spread of information and misinformation, analyzing the structure of online polarization, and developing interventions to foster healthier digital communities. It provides a comprehensive framework for understanding and mitigating the societal impact of fragmented online discourse.

Article: "AI for Detecting and Mitigating Online Polarization: Designing Interventions for Healthier Digital Discourse." This article presents advanced research on utilizing AI to detect and mitigate online polarization. It explores how machine learning can analyze communication patterns, content consumption, and network structures to identify "echo chambers" and suggest targeted interventions.

Blog Post (Current Academic Topic): "The Resilient Network: Building Online Communities Resistant to Misinformation and Polarization." This blog post academically explores interdisciplinary strategies for designing online social networks that are more resilient to the spread of misinformation and extreme polarization. It discusses the role of platform design, algorithmic transparency, and community-driven moderation.

Blog Post (Sensational/Controversial Topic): "Digital Mind Control: When Social Networks Design Your Beliefs — The Ultimate Threat to Free Thought and Democratic Will." This article provocatively discusses the highly controversial and ethically terrifying implication of advanced AI systems, informed by insights from large-scale social data, to subtly "curate" or even "optimize" social interactions at events to maximize networking, emotional engagement, or specific outcomes. It explores scenarios where AI might recommend who you talk to.

R / 02

Mentor practice lens

My publications are focused on the practical challenges of shaping 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

She possesses the "Information Diffusion Predictor," a superpower that allows her to foresee and engineer the success of digital communities. When a doctoral 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 Intervention Optimizer." This GAF-powered tool is a virtual laboratory for the computational social scientist. When a student is designing a new intervention for healthier digital communities, the Optimizer allows them to see how it will perform in the real world. It can simulate the impact of various interventions (e.g., content moderation, algorithmic nudges, community-building initiatives) on simulated user behavior, network structure, and information flow. This allows my students to move beyond the limitations of traditional, manual intervention design 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. Simone Gallo

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AI Super Mentor

Dr. Ava Clark

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Same faculty and level

Related programs

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	Doctorate This programme
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

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Advanced Social Network Dynamics and Information Propagation (Ph.D.)

Academic focus

Practical focus

Computational Social Scientist for a technology company or research institution

Data Journalist for a media outlet

Digital Policy Analyst for a government agency or think tank

Community Manager for an online platform

Leading Professor at a top-tier research university

Director of a research institute focused on social network analysis

Chief Data Scientist for a major social media platform

High-level advisor to a government or international organization on digital policy

Pioneering research and paradigm-shifting analysis

Advanced theoretical and conceptual thinking

Effective communication and leadership in the field of computational social science

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Simone Gallo

Dr. Ava Clark

Find the program that expands your universe.