Node/edge datasets in GraphML and GEXF (directed and undirected, with attributes and weights) — for testing network importers, layout tools, and graph converters.
A small synthetic social network in GraphML — 12 people in two communities with a few bridging ties, carrying node attributes (name, age, community) and weighted edges. Twinned with a GEXF file for testing graph importers and format converters.
The same 12-person social network in GEXF 1.3 (Gephi's native format) — node attributes and weighted edges. The format twin of the GraphML file, for testing Gephi imports and GraphML↔GEXF conversion.
A directed service-dependency graph in GraphML — ten microservices (web, api, auth, db, cache, queue, worker, …) with their call/dependency edges. A fixture for testing directed-graph importers, cycle detection, and topological sorting.
A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.
A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.
The consumer half of the checkout flow, in its own trace. Its root CONSUMER span carries a link — not a parent reference — back to the producer span in the checkout trace, which is how a batching consumer joins work produced elsewhere. A fixture for link resolution and cross-trace navigation.
The service map you get by aggregating the trace fixtures in this category — five service nodes and the edges between them with call counts and latency percentiles, including the messaging edge that exists only because of a span link. Ground truth for a service-graph processor.
The folded CPU profile after aggregation into the nested name, value and children hierarchy that d3-flame-graph and most web viewers consume. Every node's value equals its own self time plus its children's, which is the invariant a converter must preserve.
A CycloneDX 1.6 SBOM whose dependencies array contains two diamonds: the fictional example-logger is reached through three parents and example-json-path through two, both resolving to a single node. Every package, version, hash and licence is fictional — the tree describes nothing real.
An SPDX tag-value SBOM emphasising its relationship block: one DESCRIBES edge and ten DEPENDS_ON edges that form the same two diamonds as the CycloneDX graph fixture. Every package, version, hash and licence is fictional — the tree describes nothing real.
An SPDX 3.0 SBOM in JSON-LD — the element-graph model that replaced the 2.x document layout — describing the same fictional tree with software_Package elements and typed Relationship nodes. Every package, version, hash and licence is fictional — the tree describes nothing real.
An npm lockfile where the fictional example-cache pins a peer of example-logger@2.9.0 while the rest of the tree resolves 3.4.1, forcing a nested node_modules duplicate — the shape a deduplication report must detect. Every package, version, hash and licence is fictional — the tree describes nothing real.
A pip-compile output where every pin carries a `# via` annotation naming the requirement that pulled it in — the only place the transitive graph survives in a pip workflow. Every package, version, hash and licence is fictional — the tree describes nothing real.
An explicit node/edge dependency graph with in-degree, out-degree and depth precomputed, plus a diamonds array naming the two shared nodes and their parents — the reference answer for any graph builder run against the SBOMs and lockfiles here. Every package, version, hash and licence is fictional — the tree describes nothing real.
A dependency graph containing a genuine three-node cycle between fictional plugins — the fixture that makes a naive recursive walker hang or blow the stack, with the cycle path stated so the expected answer is unambiguous. Every package, version, hash and licence is fictional — the tree describes nothing real.
The same diamond graph as Graphviz DOT, with edge labels carrying the declared semver range and the two diamond joins filled — renderable to SVG and diffable against the JSON twin. Every package, version, hash and licence is fictional — the tree describes nothing real.
The cyclic plugin graph as DOT, with the three cycle edges highlighted. Graphviz renders it happily, which is exactly why a topological sort must be the thing that rejects it. Every package, version, hash and licence is fictional — the tree describes nothing real.
The dependency graph as Mermaid, for embedding in Markdown documentation — labelled edges carry the declared range and a classDef highlights the two diamond joins. Every package, version, hash and licence is fictional — the tree describes nothing real.
The dependency graph in GraphML, with typed attribute keys for node version, licence and depth and for edge range — the interchange format graph databases and analysis tools import. Every package, version, hash and licence is fictional — the tree describes nothing real.
The nested tree `npm ls --json` prints, where a shared dependency is REPEATED under every parent rather than deduplicated — so counting objects gives a larger number than the graph has nodes. Every package, version, hash and licence is fictional — the tree describes nothing real.
The console tree with box-drawing characters and `deduped` markers on repeat visits — the output people paste into issues, and a genuine test of whether a scraper handles non-ASCII tree glyphs. Every package, version, hash and licence is fictional — the tree describes nothing real.
Maven's dependency tree, complete with the [INFO] log prefix that has to be stripped before the tree can be read and the five-part coordinate form Maven uses. Every package, version, hash and licence is fictional — the tree describes nothing real.
Gradle's dependency report, which marks repeat subtrees with `(*)` and explains the marker in a footer — so a parser has to resolve those references back to their first occurrence. Every package, version, hash and licence is fictional — the tree describes nothing real.
`go mod graph` output: a flat edge list, one `parent@version child@version` pair per line with no indentation at all — the simplest graph serialisation in this category and the easiest to diff. Every package, version, hash and licence is fictional — the tree describes nothing real.
Cargo's tree output, which writes versions with a leading `v` and marks already-shown subtrees with `(*)` — close enough to npm's tree to fool a parser, different enough to break one. Every package, version, hash and licence is fictional — the tree describes nothing real.
pipdeptree output, whose top level is an ARRAY rather than an object — the shape that breaks a loader written against npm's tree — with both installed and required versions on every edge. Every package, version, hash and licence is fictional — the tree describes nothing real.
Every edge in the fictional dependency graph as one row — parent, child, declared range, resolved version, depth and whether the edge is direct — the form a spreadsheet or SQL import can aggregate. Every package, version, hash and licence is fictional — the tree describes nothing real.
The written explanation of both diamonds in this category — the one that resolves to a single shared node and the peer conflict that forces a nested duplicate — with the four assertions a resolver test should make. Every package, version, hash and licence is fictional — the tree describes nothing real.
An Argo Workflows DAG template carrying the same six-task ETL topology as the Airflow, Graphviz and Mermaid fixtures in this category, so a converter or visualiser can be scored against one known answer across four formats.
Argo's steps template, whose double-nested list is a genuine parser trap: the outer list is sequential and the inner list is parallel, so a reader that flattens it reports four sequential steps instead of three groups.
An Airflow DAG definition in the classic operator style, using only the no-op EmptyOperator so the file describes a topology and performs no work. Carries the six-task ETL graph shared across the Airflow JSON, Argo, Graphviz and Mermaid fixtures.
A decorator-based Airflow TaskFlow DAG where dependencies are implied by function calls rather than >> operators — the shape static DAG extractors most often get wrong. Every task returns a literal, so nothing performs I/O.
The serialized-DAG shape Airflow stores in its metadata database, as standalone JSON: per-task metadata with explicit downstream_task_ids. Carries the same six-task topology as the Python, Argo, Graphviz and Mermaid fixtures.
The shared six-task topology as a plain node/edge JSON document that also publishes the expected answers — roots, leaves, longest path, and topological order — so a graph library or DAG visualiser can be scored without a hand-written oracle.
A CWL Workflow carrying the same six-step ETL topology as the Airflow and Argo fixtures, with dependencies expressed as step output references and a multi-source input resolved by pickValue. For cross-format DAG conversion tests.
CWL scatter in both forms: a single-parameter scatter over an array input, and a two-parameter scatter with scatterMethod flat_crossproduct. The scatter dimensionality is what distinguishes correct CWL engines from approximate ones.
The shared six-task ETL topology as a Graphviz digraph, with node and edge defaults, per-node attribute overrides, a same-rank constraint and C-style comments. Carries the identical graph to the Airflow, Argo, CWL and Mermaid fixtures.
The same topology plus one back edge, so the graph is deliberately not a DAG. A renderer must still draw it and a scheduler must reject it, which is why this is a reference fixture rather than a corrupt file.
The shared six-task ETL topology as a Mermaid flowchart, with mixed node shapes, a subgraph, a classDef and %% comments. The Mermaid twin of the Graphviz fixture, for scoring diagram-format converters against one known answer.
A Mermaid stateDiagram-v2 describing environment promotion with canary and rollback transitions, start and end pseudostates, and a note block. A second Mermaid diagram type, so a renderer is not only tested on flowcharts.
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