Networking In Research

I keep returning to Damon Centola’s research on how #change spreads. Not because it’s clever. Because it’s true. Centola found that change doesn’t move like information. You can’t push it through announcements or clever messaging. It spreads through behavior, #trust, and networks. He calls it complex contagion, and it tracks with what I see inside organizations every day. People don’t change because someone at the top says so. They change when they see people they trust doing something new. Then they see it again. Then maybe one more time. That’s when it starts to feel real. That’s when it moves. Here’s what Centola’s research shows actually makes change stick: - Multiple exposures. Once isn’t enough. People need to encounter the new behavior several times from different people. - Trusted messengers. It’s not about role or rank. It’s about credibility in the day-to-day. - Strong ties. Close, high-trust relationships are where change actually moves. - Visible behavior. People need to see it being done, not just hear about it. - Reinforcement over time. Real change takes repetition. One wave won’t do it. This flips most #ChangeManagement upside down. It’s not about the rollout or coms plan. It’s about reinforcing new behaviors inside the real social structure of the organization. So, if you are a part of change, ask your team and self: 1. Who are the people others watch? 2. Where are the trusted connections? 3. Is the behavior visible and repeated? 4. Are you designing for reinforcement or just awareness? Change isn’t a #communication problem. It’s a network pattern. That’s the shift. That’s the work. And that’s what I help teams build.

The reason AI hasn't transformed science faster isn't what you think. It’s not because of: ❌ a lack of compute ❌ a lack of data ❌ not-good-enough models ❌ not enough AGI The real problem is social. This summer, Avijit Ghosh, PhD and I wrote a whole position paper about this, but here's the gist: 👩💻 Machine learner: “All your problems can be solved with my ML.” 🧑🔬 Scientist: gives a impenetrable 30-minute jargon-filled talk. The ML researcher zones out → “So… what do I optimize?” The scientist throws up their hands. Nothing happens. But, hope isn’t lost. We can learn to communicate better, and it starts with sharing spaces. Machine learners and scientists need to share the same reading lists, the same problems, and the same social spaces. So, we started building hugging-science, a community where machine learners and scientists actually talk and build together. In just one month: 🤝 1 Discord server 🤝 5 major projects launched (so far) 🤝 700 contributors 🤝 5 Spaces 🤝 4,000+ members bringing wildly diverse expertise And we’re already seeing progress. More scientific datasets are being shared openly on Hugging Face. We built a space to track progress, and some of the biggest contributors are Polymathic AI, Entalpic, Bo Wang, and Kevin Maik Jablonka. 👏

I stopped obsessing over publishing… and that’s when my research career took off. (Here’s what I discovered) Four years ago, I believed publishing was the only path to academic success. My inbox? Empty. My collaborations? Stagnant. My impact? Limited to footnotes in other people’s papers. Then, I did something radical: I shared my work outside journals. A blog post about the LCA work I did for a partner. A LinkedIn post breaking down techno-economic assessment and process design methods. A webinar sharing my research outputs. Crickets. For weeks. Until a founder DM’ed: "Liked the recording of your webinar. Can you do something like this for us to verify our TEA?" A week later, an academic mentor slid into my DMs: "Saw your recent work on carbon capture. Can we co-write a research bid?" I wasn't sure what to do. This wasn’t “real” academic work. I’d been pre-conditioned to share my work only in scholarly journals and conferences. But suddenly, my research was solving problems, not merely gathering dust. So I leaned in. I built a simple system:  1. Every paper made available as PDF with posts on problem, method, outputs  2. Conference slides became PDFs shared with key takeaways  3. Complex science converted into trade magazines and blog posts The response? A FTSE 250 energy company invited me to perform a market study for their new direct air capture business strategy. Learned societies invited me as a keynote speaker for their events. And yes, citations keep coming, but now tied to real-world impact. Here’s what academia won’t tell you: Visibility isn’t vanity. It’s the bridge between your work and the problems it can solve. You don’t need 100 papers to make a difference. You need the right people to see your work. Now? I teach researchers to build this bridge. Because your career shouldn’t hinge on how many journals you’ve cracked. It should hinge on how many minds you’ve changed. Start there. Let the papers follow. P.S. What is the most significant challenge that pre ents you getting your research seen by others? #science #scientist #research #publishing #phd #postdoctoral #professor #academia #highereducation

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

Rice, Roti, and the Invisible Architecture of Family Life (How everyday food formats may quietly shape who sits, who waits, and who belongs ) some of the most revealing socio-cultural insights don’t come from explicit beliefs or stated values, but from the quiet mechanics of everyday life. Take something as mundane as rice versus wheat. When you overlay maps of rice-eating regions with other social indicators, it opens up an interesting hypothesis space — not causal, not deterministic, but deeply suggestive. Rice, by its nature, is cooked in one go. It allows meals to be timed, shared, and eaten together. The family gathers, sits at the same table, and participates in the act of eating simultaneously. Wheat, especially in the form of rotis, operates very differently. Rotis need to be made fresh and served hot, which almost always requires someone to stay back in the kitchen while others begin their meal. In most households, that role has historically fallen to women. Over time, this creates a subtle but persistent segregation — kitchen versus dining space, service versus consumption, waiting versus eating. None of this is imposed as ideology; it emerges as routine. The hypothesis is not that wheat causes patriarchy or rice creates equality, but that food formats can quietly reinforce existing social structures by shaping who sits where, who waits, and who eats together. These micro-behaviours, repeated daily over generations, may end up encoding power, gender roles, and participation far more effectively than any spoken rule ever could. Thoughts ??

We spend a lot of time designing organizations. Structures. Reporting lines. Governance.   And yet, when performance slows down or transformations stall, the issue is rarely the formal design.   It is the network beneath it. Who people trust. Where information really flows. Who connects teams and where friction quietly builds.   In every organization, there is an “invisible layer” that determines how things actually get done.   In my experience, understanding this layer is one of the most powerful and most underused levers to improve performance.   Over the coming weeks, we will run an Organizational Network Analysis (ONA) in parts of our organization.   This is not about evaluating individuals. It is about understanding how the system works so that we can remove friction, strengthen collaboration, and operate at our full potential.   If you are interested in the thinking behind this, one of the most practical articles on the topic is in the comments.    When you make the invisible visible, performance follows.

Don't reach out cold until you have spoken and reconnected with all these people first and have asked for AIR + explained exactly what you are looking for. 🎈 Advice - career advice, job search advice, career pivot advice 🎈 Information - What's going on in the company, industry? What trends are they seeing? 🎈 Recommendations - Who do they recommend you speak with to learn more? What professional associations or groups do they recommend? Where do they go or what do they read to stay current? 🎯 Contact ALL these People So They Know What You Are Looking For 1. People You Used To Work With Your past work colleagues have seen you perform in the job and know your strengths and work ethic. These people make an excellent source of information to find out what changes are going on in the business and industry. You want to let them know you are looking for a new opportunity. 2. Friends, Family, Neighbors People you know are most likely to want to help you if they can. Your friends have a vast network of contacts you don’t know about. 3. Past Managers Assuming you and your previous manager or supervisor got along, it’s a good idea to reach out to them. Your past boss may know of upcoming opportunities at your old company 4. Target Company Employees Talking with people who work inside a company you're interested in allows you to learn what it is really like to work there. Plus they can provide advice and/or insight on the best way to apply. 5. Alumni/Classmates Don’t forget to tap into classmates, professors, and alumni as another potential pool of people to network with. Use LinkedIn's Alumni tool plus your school’s Advancement or Alumni office database. 6. Customers/Clients The people you’ve served already know you and are familiar with your work. Lean on them as a source of information about what’s going on. Your customers and clients have a feel of the work landscape and future needs. This information will help you position your most important skills and experience. 7. Vendors/Suppliers Similar to your clients and customers, these people know what it’s like to do business with you. They also have a finger on the pulse of what’s happening in your industry because they are still servicing businesses. 8. Service Providers (Doctors, accountants, hairdresser, etc.) Don’t overlook the business relationships you have with professionals who provide you with services. These people have their own vast network of contacts. 9. Fellow Volunteers If you volunteer, you’ve likely established relationships with other volunteers and people within the organization. These people have seen you give your time and effort.

Good policy listens. Great policy listens to science.  In a complex world, policies that learn from evidence remain credible, resilient and trusted. That’s why it’s so important for EU-funded researchers and innovators to engage more closely with policymakers. My colleagues at the European Research Executive Agency (REA) produced a series of practical guides to help projects communicate, disseminate and exploit their scientific results more effectively - and ensure that their work reaches those who shape policy and support their uptake in the economy.  1️⃣ Starter kit with step-by-step advice on sharing results with policymakers, an overview of key resources and tips to amplify the impact of scientific evidence:  https://lnkd.in/duXKbFap  2️⃣ Guide on writing policy briefs, including drafting steps, dos and don’ts and a handy template with examples:  https://lnkd.in/d46ERqPy  3️⃣ ️ To engage a broader range of stakeholders beyond policymakers - such as academia and industry - a special starter kit for disseminating and exploiting scientific results was created. It offers practical tools, resources and hands-on guidance: https://lnkd.in/d_yiqTPy  The goal is simple: help good science travel further, faster and turn evidence into action. 

One of the best things I tell students and early career professionals when we talk about networking is this: Stay Curious, Not Clingy. Don’t just slide into someone’s inbox with “Can I pick your brain?” or even worse, just “Hi.”. A good question can open more doors than a long CV ever could. Instead of: "Hi, can you help me get a job in PR?" Try this: "Hi, I admire your work in strategic communications. What advice would you give someone trying to build thought leadership early in their career?" That tiny shift communicates initiative, respect, and curiosity—qualities that industry professionals notice and remember. Last month, I had the privilege of training university students of BlueCrest University College on how to build genuine connections that lead to internships, collaborations, and even jobs, and this was one of my key points: The goal isn’t just to collect contacts. It’s to build context and credibility. So next time you're tempted to reach out and ask for help, pause and ask a thoughtful question instead. In a world where nosy people don’t network well, you’ll stand out—and you might even get more than you hoped for. #KeniRibeiro #NetworkingTips #EarlyCareer #CareerServices #ProfessionalDevelopment #CommunicationsStrategy #ThoughtLeadership #BrandStrategist

The World Health Organization Hub in #Berlin: driving innovation to make the world safer from health threats WHO is developing new tools and innovative partnerships to boost countries’ defenses against future #pandemics, including real-time #threat #detection and #genomic analysis of #viruses In today’s interconnected world, health threats spread faster than ever. A new virus can cross continents in hours. An outbreak in one country can escalate into a global crisis in days. This reality requires constant innovation to protect lives and prevent the next pandemic Building on lessons learned from the #COVID-19 pandemic, the WHO Hub for Pandemic and Epidemic Intelligence in Berlin leverages innovative tools and collaborations for more effective disease surveillance worldwide. Just over three years after its inauguration, the Hub now supports over 150 countries in detecting health threats more effectively and rapidly The Hub’s latest annual report highlights the growing impact of this work and provides key insights into progress made in 2024 As no country can tackle the next pandemic alone, WHO is supporting countries to implement Collaborative Surveillance, a new collaborative approach to disease surveillance that promotes data and information sharing so that outbreaks can be detected and controlled faster The early warning system hosted at the Hub, called Epidemic Intelligence from Open Sources, scans online sources in real time and uses #AI technology to identify #publichealth threats more efficiently “The Hub is ensuring that the most robust tools and analytics are available to enhance early threat detection and rapid response and support decision-makers around the world,” said Tedros Adhanom Ghebreyesus, WHO DG. “I have urged all WHO Member States to work closely with the Hub, not only to strengthen their own national and regional health security, but also to contribute to global preparedness and response.” Pathogen genomics, which analyses the genetic material of viruses and other pathogens, has become a powerful tool to track and predict outbreaks. The Hub’s International Pathogen Surveillance Network connects over 235 organizations and countries to expand genomic surveillance more equitably around the world, including through a US$ 4 million fund for low- and middle-income countries To help decision-makers better understand an emerging #health #emergency and plan an effective response, the Hub is developing a cutting-edge platform that will visualize disease transmission and simulate the impact of different countermeasures. Once launched, the pandemic simulator will provide actionable insights to policy-makers and support them in responding to a health #crisis WHO remains at the forefront of developing tools, building partnerships and strengthening public health intelligence and surveillance capacities worldwide