Collaboration Graph¶

A graph whose vertices are declared participants and whose edges represent a specified pairwise collaboration relation within a stated scope and time window.

Version: v1 · 2026-09-28 · History
Domain-specific #: 8541
Domain group: Social Sciences
Origin domain: Sociology & Anthropology
Subdomain: Social Network Analysis → Sociology & Anthropology
Aliases: Collaboration network, Coauthorship graph, Coauthorship network

Core Idea¶

A collaboration graph translates joint activity into network topology. Vertices may be people, institutions, countries, or another participant level. An edge joins two distinct vertices when they satisfy a declared collaboration rule, such as coauthoring a paper, appearing in a film, or playing on the same team within the selected data scope.

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Who-Worked-Together Map

Draw a dot for every person. Whenever two people have worked on something together, like making a movie or writing a book, draw a line between their dots. The picture you get is a Collaboration Graph. You can follow the lines to see how many steps it takes to get from one person to another.

Who-Worked-With-Whom Map

A Collaboration Graph is a map of who has worked with whom. Each dot stands for a participant, like a person, a school or a country, and a line joins two dots if they worked together under a chosen rule, like writing a paper together or playing on the same team. Working together many times still gives just one line in the simple version, and nobody gets a line to themselves. Some dots may have no lines at all, and some groups might not connect to others. The distance between two people is the fewest lines you need to follow to get from one to the other.

Joint-Activity Network

A Collaboration Graph turns joint activity into a network. The vertices are participants at some chosen level, such as people, institutions or countries, and an edge connects two different vertices when they meet a stated collaboration rule, for example coauthoring a paper, appearing in the same film, or playing on the same team within the chosen data. In the basic unweighted version, repeated collaborations do not create extra edges and no vertex is linked to itself. The graph may have isolated vertices and separate pieces, and the distance between two vertices is the length of the shortest path, which is infinite if they are in different pieces. Weighted or time-based versions can record how often or when people collaborated. Features like a big connected core, lots of clustering and short average distances have been found in some real collaboration networks, but they are observations, not part of the definition.

A Collaboration Graph is a simple graph whose vertices are participants at a declared level of aggregation, such as individuals, institutions or countries, and whose edges join two distinct vertices that satisfy an explicit collaboration rule within a stated data scope, for instance coauthorship, co-appearance in a film, or membership on the same team. The basic structure is an unweighted projection of joint activity: multiple collaborations between the same pair collapse to one edge, and self-collaboration produces no loops. Isolated vertices and disconnected components are allowed. Graph distance is shortest-path length and is infinite between vertices in different components. Weighted variants encode collaboration frequency and temporal variants encode timing. High clustering, a giant component and small average distances are empirical regularities found in some collaboration systems, not requirements for something to count as a collaboration graph. The definition therefore hinges on the chosen vertex level, collaboration rule and data scope.

Scope of Application¶

Coauthorship networks. Authors, institutions, or countries are linked through joint publications.
Creative industries. Actors or creators are linked through shared productions.
Sports networks. Players can be linked by service on the same team under a time rule.
Research evaluation. Components, brokerage, collaboration distance, and cross-group ties describe network organization when data coverage is appropriate.

Clarity¶

Construction should declare actor level, identity resolution, event definition, authorship or participation threshold, date window, edge direction, weight, and handling of large teams. A lifetime simple graph answers a different question from a yearly weighted graph. Metrics inherit errors from missing records and merged or split identities.

Manages Complexity¶

The graph compresses many multi-person events into vertices and edges, making reach, components, clustering, and paths computable. Pairwise projection can dramatically inflate ties from a single large collaboration and erase which event created each edge. Bipartite or hypergraph models restore event membership when that distinction matters.

Abstract Reasoning¶

Define eligible participants, collaboration event, scope, and time window.
Resolve identities and preserve provenance for each participant–event link.
Choose simple, weighted, temporal, bipartite, or hypergraph representation.
If projecting events, specify how each event creates and weights pairwise edges.
Compute distances, components, degrees, clustering, or centrality with disconnected cases handled explicitly.

Knowledge Transfer¶

The graph construction transfers across scholarly, artistic, athletic, and organizational domains when vertices and joint events are explicit. A communication or affiliation graph may be analyzed similarly but is not a collaboration graph unless its edge relation is genuinely collaborative. The graph-theoretic metrics transfer more broadly than the social meaning of an edge.

Relationships to Other Abstractions¶

Current abstraction Collaboration Graph Domain-specific

Parents (1) — more general patterns this builds on

Collaboration Graph is a kind of Network Prime

A Collaboration Graph is a Network whose nodes are participants and whose edges encode a declared collaboration relation.

Hierarchy path (1) — routes to 1 parentless root

Collaboration Graph → Network → Reservoir-Flux Network → Conservation Laws → Invariance

Neighborhood in Abstraction Space¶

Collaboration Graph sits in a moderately populated region (47^th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.

Family — Organizational Patterns & Management Concepts (29 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-10-08