Network Analysis in Social Research

I have some observations and an anecdote for those who are currently invested in studying or learning, at any stage of their life. Learning is an order of magnitude more effective if you have a clear purpose or use case for which you need the knowledge/skill. This is one core reason why many of us quickly lose knowledge that we gain during academic study, and it is also a reason why some packaged 'certifications' can be ineffective at ensuring you retain knowledge and skills. The right purpose/use case needs to be a problem you are excited about and highly incentivised to solve, and often you can come up with these for yourself. Let me give you an example. Early in 2020, my 11 year old daughter and I were watching an episode of Friends, when she started talking about all the relationships between the characters and how complex they became as each season progressed. At the time, I was keen to build my technical skills in Network Analysis, and I had extra time on my hands because of the pandemic. I realized that my daughter's observation would be a great data science and data visualization project. Over the next few weeks I decided to build a codebase for this project, including: 1. Learning how to scrape character appearances from scripts of Friends episodes in R 2. Determining how to turn the data into a network edgelist using R 3. Constructing and analyzing graphs and graph metrics from the edgelist using R 4. Using iterative and functional programming to automate these processes across multiple seasons and episodes of Friends. 5. Building out an interactive visualization of how the network complexity increased with each season (below) using D3 and Javascript. Later I built out the equivalent codebase in Python also, and it became one of the learning components of my textbook on graph theory and network analysis. Because of the experience I had in building a project like this from nothing, and in bringing the truth of data to my daughter's observations, this became a permanent knowledge and skill foundation for me which I still make great use of today in my job and in my teaching. If you are interested in exploring the original codebase of this work, you can find it here ---> https://lnkd.in/dhNcm8E. The final visualization is here --> https://lnkd.in/dKdJXj3. For a more in-depth tutorial and treatment of this work, you can consult my textbook here --> https://ona-book.org #analytics #learning #datascience #rstats #python #peopleanalytics #networks #technology

Bibliometric network analysis can reveal the sociology of a research field as much as its science. These two citation graphs, both generated automatically from OpenAlex data, show strikingly different structures. The ultra-processed food literature has a classic hub-and-spoke topology: a dense core of foundational work that the rest of the field cites inward, with little lateral citation between peripheral authors. The polyphenol subgroup looks entirely different: three well-defined research communities with substantial internal cohesion and active bridges between them. That's what a mature, multi-strand field looks like — parallel traditions that are aware of and building on each other. The interactive explorer behind these graphs supports author search, shortest-path finding between any two researchers, colour-coding by country or research cluster, and clicking any edge to see the papers behind a citation link. All data is open (OpenAlex), all code is Python. https://lnkd.in/ecbjrzYG

Who do you turn to for answers at work, and who actually inspires you? When we ask these two questions through active Organizational Network Analysis (ONA), the networks that emerge look completely different. One shows how information flows. The other shows where energy and inspiration come from. Both views matter. Information explains how work gets done. Inspiration explains why people give their best. Passive ONA, based on metadata, is great at showing the first. Active ONA, based on surveys, is essential to uncover the second. The ideal is to combine both lenses. Only then can we see the full picture of how organizations really work and what makes them thrive. #PeopleAnalytics #OrganizationalNetworkAnalysis #FutureOfWork #SocialCapital

📚 Sundays are for reading. And today’s paper is about something that I have always grappled with—how do choices at an individual level square with large-scale, population-level behavioral shifts? From a methodological perspective, these two fields have always stayed separate. There are the scientists (e..g the behavioral economists) who study individual choices. They focus on what a person believes, their biases, their lived situation, and what types of nudges or context shifts will push them to adopt a behavior. Then there are the scientists concerned with a systems view. They look at how information and norms move through networks. They focus on population adoption, using complexity science to model how a behavior "goes viral." And here is what made this challenging… If you only look at the individual, you miss the "social signal". The fact that most of us don't change until we see others doing it. But if you only look at the network, you treat everyone like a uniform "node," ignoring that some people are naturally more resistant to change than others. Let me make this real. Think about a public health campaign. Complexity science tells us to target "influencers" to seed the behavior. But if those influencers are surrounded by people with a high "resistance threshold," the movement dies in its tracks. You can have the best network strategy in the world, but if it doesn't account for individual "tipping points," it won't scale. What these authors propose is brilliant! Radu Tanase and the team at the University of Zurich have finally integrated Discrete Choice Modeling (one of my favorite methods!) directly into Dynamical Network Models. 🎉 They used behavioral experiments to measure individual "adoption thresholds", exactly how much social proof a specific person needs before they flip. Then, they plugged those human thresholds into social network simulations. And walla! They found that strategies combining network structure with these individual thresholds consistently outperformed traditional "influencer" seeding. Understanding who is ready to change and their specific “why” is just as important as knowing who they are connected to. Link to the paper published in Nature Human Behavior in the comments 👇. Happy reading! #SocialChange #BehavioralScience #NetworkScience #NatureHumanBehaviour #SundayReading #ComplexityScience

Now out in Nature Human Behaviour! 🚀 🚀 Over the past decades, research on collective human behaviour has relied heavily on networks. This is intuitive: people interact with other people. However, we argue that this dominant framework misses a crucial ingredient. Traditional networks represent agents as nodes and pairwise relations as edges. As a result, they fundamentally assume that social interactions can be decomposed into pairs. Yet many social processes are irreducibly group-based. A simple example: a group of three coauthors writing a paper cannot be reduced to three independent pairs of coauthors. The group itself matters. In this article, we review a wide range of empirical and theoretical cases where group interactions cannot be decomposed into pairwise ones, and show that higher-order interactions shape collective behaviour above and beyond dyadic ties. We advocate studying collective behaviour on hypergraphs, where interactions can involve multiple agents simultaneously. We review how hypergraphs provide new insights across domains, including affiliation and collaboration networks, high-frequency contact settings (families, friends), and key social processes such as social contagion, cooperation, truth-telling, and moral behaviour. Finally, we outline promising directions for future research: addressing computational challenges of higher-order models; studying bias and inequality in group dynamics; combining hypergraphs and large language models to investigate the coevolution of language and behaviour; using higher-order networks to simulate the impact of policies before implementation; and others. We are very excited about this work and hope it will inspire further research in a rapidly growing and fundamental area with broad real-world implications. Link to the full paper in the first comment. This work was brilliantly led by Federico Battiston, with an outstanding team of co-authors: Fariba Karimi, Sune Lehmann, Andrea Bamberg Migliano, Onkar Sadekar, Angel Sanchez, and Matjaz Perc

🚀 Super excited to share our latest contribution to advancing sustainability in South Africa! 🌍 Our recently published paper delves into the critical need for integrated and inclusive financing across the water, energy, and food (WEF) nexus to achieve just transitions in South Africa. Through a Social Network Analysis approach, we examined how multi-actor institutional financing structures shape equity and coordination within this interconnected sector. Key takeaways: Sparse financing networks with limited collaboration across decision-making levels. The central role of key intermediaries like the Public Investment Corporation and Land Bank in financing WEF-related projects. Local-level financing is largely absent, signaling the need for stronger institutional synergies and cross-sectoral partnerships. The call for regional financing hubs to bridge gaps and align funding systems with local development priorities. Our findings are critical for policymakers and financial stakeholders working to realign funding systems with sustainable development goals and accelerate equitable transitions. 📝 Full paper available: https://lnkd.in/dM9MR8Ck #Sustainability #Finance #WaterEnergyFood #JustTransitions #SouthAfrica #InclusiveFinance #PolicyResearch #NetworkAnalysis

The social signals behind employee retention "Research has long shown that employees at the center of an organizational network—those with many active connections—are 24 percent less likely to leave." 🤔 Michael Arena and Aaron Chasan highlight an important insight: employee connection, not just engagement, is the true bedrock of retention: 👉 “In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door.” For HR to genuinely impact business performance and employee experience, we must leverage social signals to build robust internal networks. Michael and Aaron outline four high-impact ways HR can proactively employee connection and significantly reduce attrition: 🔎 Utilise network analysis: Identify early flight risks by spotting employees with few or declining connections. 🔎 Facilitate connection moments: Deliberately create opportunities for interaction, especially in hybrid settings, using tools like interest-based matching. 🔎 Support relationship-rich teams: Encourage cross-functional initiatives and invest in psychologically safe team cultures. 🔎 Routinely pulse central employees: Their engagement profoundly influences the entire network. "In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door." 👉 This report is featured in the June edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/exEqY-Hn 👈 #humanresources #organizationalnetworkanalysis #peopleanalytics #leadership #culture #socialcapital

📘 New in #JDevStudies How does social capital influence farmers’ access to services and coordination in agricultural markets? Samuel Ahoudjo and Guillaume Soullier present a comparative analysis of rice value chains in Côte d’Ivoire and Ghana, focusing on seven farmer organizations. Using network analysis, they show how different network structures shape access to resources and partnerships with market actors. Key findings: ✅ Social ties have a stronger role in Côte d’Ivoire than in Ghana ✅ Organizations with decentralized networks work more with wholesalers ✅ Centralized networks are more likely to sell directly to consumers ✅ Leadership, trust, and governance are critical for effective coordination Read more ➡️ : https://lnkd.in/eAjFuQcX #Agriculture #SocialCapital