Repeat partnerships are visible everywhere in the record of funded EU projects, and they are not an accident. Map past Erasmus+ and Horizon Europe consortia as a network and funded projects stop looking like lists of partners: they look like structures, most often a stable core of organisations that have worked together before, surrounded by a periphery of partners who appear once and move on. Open data cannot prove that repeat ties cause success, because rejected consortia leave no public trace. What it does show, clearly and repeatedly, is that experienced consortia are built around repeat ties, and that coordinators who think in networks assemble their partnerships differently from coordinators who think in lists.
Most partner searches run as a shopping exercise. The coordinator opens a directory, filters by country and organisation type, collects expressions of interest, and fills the empty rows in the application form. Each partner is evaluated on its own merits: track record, capacity, thematic fit.
That evaluation misses the thing that determines how the project will actually run. A consortium is a temporary organisation. Budgets, deliverables and blame flow through relationships between partners, not through their individual profiles. The questions that decide whether month nine goes smoothly are relational: who has worked with whom before, who trusts whose financial reporting, who will pick up the phone when a partner goes silent.
So the unit of analysis should be the tie, not the organisation. Two mid-sized partners who have delivered two projects together are, for coordination purposes, a different kind of asset than two impressive strangers. A partner list hides this. A graph shows it.
We maintain a consortium graph built from CORDIS and Erasmus+ open data, and it is worth being precise about what it encodes, because network language turns vague quickly.
Everything the graph reveals about consortium formation rests on that distinction between a tie and a repeat tie.
Look at enough consortia through this lens and a few shapes come back again and again.
The most common is the stable core with a rotating periphery. A small set of organisations forms the core: they appear together across projects, calls and years, and they typically hold coordination, quality assurance and financial management. Around them, each new project adds peripheral partners chosen for what the call requires: a country to complete the geographic spread, a target group, a pilot site, a sector voice. Peripheral partners often appear once and are not seen with that core again. That is not a flaw in the model; it is the model. The core carries the machinery, the periphery carries the specificity.
The second shape is the broker: an organisation sitting between clusters that otherwise never touch. A network of universities and a network of municipalities may share no direct ties at all, while one intermediary, often an NGO or an association, holds repeat ties into both. Brokers are how newcomers actually enter EU projects, far more often than through cold outreach: a broker vouches for you on one side because it is trusted on the other.
The third shape is the closed clique: a group that partners almost exclusively with itself. It coordinates cheaply and delivers predictably, and over time it can start to read as a circle re-funding itself, with little new blood and shrinking reach.
The graph does not tell you whom to invite. It tells you where to place people.
Before submission, spend an hour turning your list into a picture. It requires no tooling beyond patience.
Step 1. Draw your partners as points and connect every pair that has shared a funded project before. CORDIS and the Erasmus+ project results platform both let you check an organisation's project history.
Step 2. Find your core. If the connected pairs do not include the partners holding coordination-critical work packages, you are running critical machinery on untested relationships, and your risk section should say how you will manage that.
Step 3. Find your isolates: partners connected to nothing. Each one is a real coordination cost. Decide, by name, who onboards each isolate.
Step 4. Check for a broker. If your consortium spans two communities, someone must be credible in both. If nobody is, the seam between the two halves is where the project will strain.
Step 5. Reread the work plan against the picture: strong ties carrying critical work, newcomers in bounded roles, no partner floating free. That shape is what experienced consortia converge on.
Open data records participation, not performance. The graph cannot see delivery quality: an organisation can appear in many projects and have been carried through every one of them. It cannot see internal conflict, near-misses, or the partner quietly dropped from the next proposal. It cannot see staff turnover, and ties really live between people: when the project manager who built a relationship leaves, the tie on paper outlives the tie in reality. And it only ever shows funded consortia, so every pattern in it is a pattern among survivors.
This is why the network lens complements, and never replaces, partner due diligence: reference conversations with previous coordinators, checking that claimed deliverables are actually public, asking which named people will staff the work. The graph tells you where to look and which questions to ask; the answers still come from humans. Structural partner analysis of this kind is part of the grant intelligence work we do at StrandsUnited for coordinators preparing applications.
StrandsUnited maintains a consortium graph covering 114,731 organisations and 83,490 projects, built from CORDIS and Erasmus+ open data for the 2014-2027 programming periods. The shapes described here are the ones we see recurring in that graph, tempered by what we learn operating community platforms such as Impactful, where the coordination problems behind these network patterns play out in daily practice.