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

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

What use is people analytics if you don’t use it to validate watercooler and overheard gossip? Since last month, I have been doing a lot of reading and experimenting with network analysis to visualize strength and frequency of communication between manager and their direct reports. The video in this post is an animated analysis I did in python which validates employee grievances that managers only interact more often and more meaningfully during performance review cycle (in March, month 3) or engagement survey cycle (in September, month 9). The numerical values on the edges represent the normalized communication strength (on a scale of 0.1 to 1.0). Higher values (e.g., 0.9): Strong communication during key periods like performance evaluations and surveys. Lower values (e.g., 0.2): Minimal communication during off-cycle periods. You’ll notice edges become thicker and more connected in March and September, which correspond to performance evaluation and survey periods. Other Months: Communication is lighter, reflected by thinner edges and reduced connectivity. This visualization highlights how communication within the organization fluctuates based on critical periods, offering insights into patterns of engagement and interaction. #peopleanalytics #organizationnetwork #hr #dataanalytics

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

Graphs are just the beginning. Graph algorithms unlock deeper insights. Graphs and graph databases are unmatched for understanding connections in your data. 🚀 But you can push those insights even further using graph algorithms. Unlike traditional algorithms, 𝗴𝗿𝗮𝗽𝗵 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗮𝗿𝗲 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝘄𝗶𝘁𝗵 𝗻𝗼𝗱𝗲𝘀 𝗮𝗻𝗱 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀, uncovering insights that are impossible to achieve through rows and columns alone. These are the main categories of graph algorithms you should know: ☑️ 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘁𝘆: Measures node importance based on connectivity or influence. They are key for identifying influencers in social networks, critical infrastructure points, or outliers in the data. ☑️ 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 / 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼n: Groups nodes based on their relationships. They are ideal for detecting communities, subgraphs, or clusters that signal patterns like fraud or collusion. ☑️ 𝗣𝗮𝘁𝗵𝗳𝗶𝗻𝗱𝗶𝗻𝗴: Finds the shortest or most efficient paths between nodes. They are useful for optimizing routes, network flows, or even supply chains. ☑️ 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆: Compares nodes based on their attributes or connections. They are often used for recommendation engines or as part of a sequence of algorithms. ☑️ 𝗗𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀: Identify graphs that do not have cycles (loops). These types of graphs are often used for workflows, task scheduling, and version control. ☑️ 𝗟𝗶𝗻𝗸 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻: Predicts missing or future connections between nodes. These algorithms are often applied in recommendation systems, social networks, or biological research. ☑️ 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: Converts graph structures into vectors, preserving their relationships for machine learning. They are essential for applying machine learning techniques to graph data. ☑️ 𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗚𝗡𝗡𝘀): Deep learning models tailored for graphs. They enable advanced tasks like node classification, graph classification, and sophisticated link prediction. Graph algorithms can take your data analysis to the next level, driving better insights and smarter decisions. Whether you're optimizing processes, detecting fraud, or improving recommendations, graph algorithms deliver insights that traditional methods simply can’t match. 💬 How have you used graph algorithms in your projects? Share your experiences in the comments. ♻️ Know someone who could benefit from using graphs or graph algorithms? Share this post with them. 🔔 Follow me, Daniel Bukowski, for daily insights on generating value from connected data.