Graph Neural Networks Certification

The Graph Neural Networks Certification is a free advanced-level, 25-minute skill test from Science of Apps. It tests graph neural networks — graph representations and tasks, node embeddings such as node2vec, the message-passing framework, GCN, GraphSAGE, GAT and GIN layers, expressive power and the Weisfeiler-Lehman test, over-smoothing and over-squashing, graph transformers, scaling with neighbour sampling, link prediction and evaluation leakage, and applications in molecules, recommendations, fraud and knowledge graphs — written to the GCN, GraphSAGE, GAT, GIN, MPNN, OGB and PinSage papers for 2026 and 2027. Score 70% or more on its 25 questions to earn a verifiable Graph Neural Networks certificate (SOA-GNN). Every question is based on published standards and research — including Open Graph Benchmark: Datasets for Machine Learning on Graphs (Hu et al., 2020) (arXiv), PyTorch Geometric documentation (PyG Team), How Powerful are Graph Neural Networks? (GIN, Xu et al., 2019) (arXiv) — and each answer is explained with its source.

Science of Apps offers a free Graph Neural Networks Certification, written for 2026 and 2027. It tests graph foundations, message-passing architectures, depth, scale and graph transformers, training, evaluation and pitfalls and applications and responsible use with 25 original scenario questions in all, each naming its published source; you get an instant score and a verifiable certificate with a credential ID you can add to LinkedIn.

Read the five skill areas on this page, then take the 25-minute test online — no fee, course or sign-up. You see every answer explained when you finish. Score 70% or more and you get a Graph Neural Networks certificate straight away, on screen, as a PDF and by email, with a credential ID anyone can verify.

Yes. The questions are original scenarios written by Science of Apps, and each one is based on a public source you can read: Open Graph Benchmark: Datasets for Machine Learning on Graphs (Hu et al., 2020) (arXiv); PyTorch Geometric documentation (PyG Team); How Powerful are Graph Neural Networks? (GIN, Xu et al., 2019) (arXiv); Semi-Supervised Classification with Graph Convolutional Networks (Kipf and Welling (arXiv 2016)); Inductive Representation Learning on Large Graphs (Hamilton, Ying and Leskovec (arXiv 2017)); Do Transformers Really Perform Bad for Graph Representation? (Graphormer, Ying et al., 2021) (arXiv). The source is shown under each question and linked in the answer review. Science of Apps doesn't copy questions from other tests.

Five skill areas with five questions each, drawn from a bank of 10 per area so a retake brings new questions: Graph Foundations (Graph data and tasks, Node embeddings and Representations); Message-Passing Architectures (GCN, GraphSAGE and GAT and Expressive power); Depth, Scale and Graph Transformers (Over-smoothing and over-squashing, Scaling to large graphs and Graph transformers); Training, Evaluation and Pitfalls (Splits and leakage, Benchmarks and Link prediction); Applications and Responsible Use (Molecules and science, Recommendations and fraud and Privacy and fairness). Every question is an original scenario written to the 2026–2027 standard, with the published source named under it.

ML engineer working with graph data, Data scientist in fraud, risk or networks, Computational chemist or drug discovery scientist, Recommender systems engineer and Graduate student in machine learning. It suits students at leading universities, career switchers and experienced professionals who want quick, verifiable proof of Graph Neural Networks knowledge in 2026 and 2027.

CS and AI students at MIT, Stanford, CMU, Berkeley, IITs, Tsinghua and ETH are hired on depth beyond tool use: model internals, systems and hardware. Passing earns a founder-signed Science of Apps certificate with a credential ID and QR code that recruiters can verify.

In each layer, every node aggregates "messages" computed from its neighbours' features, then updates its own representation by combining the aggregate with its previous state. Stacking k layers lets information flow from nodes up to k hops away.

Repeated neighbourhood averaging makes node representations increasingly similar (over-smoothing), and information from many distant nodes must be compressed into fixed-size vectors (over-squashing). Residual connections, normalisation, rewiring and attention can help.

Yes. The test is online, in English and free everywhere — people take it from India, the US, the UK, Europe, Africa, Latin America and Asia-Pacific. Your certificate is dated with your own local date and can be verified from anywhere at scienceofapps.com/certification/verify/.

25 questions — 5 from each of 5 skill areas — with a 25-minute limit. You need 70% (18 of 25) to pass and 90% for distinction.

We rate it Advanced: assumes years in the role or deep specialist knowledge. It has 25 scenario questions — 5 from each of 5 skill areas — and a 25-minute limit. You need 70% (18 of 25) to pass and 90% for distinction.

Nothing. There is no fee to take a test, to receive the certificate, to have it emailed or to verify it, and no sign-up.

On our server, not in your browser. The answers never reach the page until you submit, so a certificate can only be earned by passing. Each certificate gets a unique credential ID stored by Science of Apps.

You see your score for each area and every question with the correct answer and why, with the published source behind it. You can retake after 30 minutes, and the retake draws questions you haven't seen yet.

No. Every certificate is dated, so it always shows when you passed. Retake a test any time for a newer date or a higher score.

Yes. After you pass, Add to LinkedIn opens the Licenses & certifications form with the certificate name, Science of Apps as the issuer, the issue date, your credential ID and the verification link filled in. You can also download it as a PDF or PNG, and it's emailed to you as a PDF.

Every certificate has a credential ID and a QR code. Scanning it, or entering the ID at scienceofapps.com/certification/verify/, shows the holder's name, the certification, the score and the issue date from our records.

No. They are Science of Apps' own assessments of applied skill. They aren't issued by a university or standards body, and they work best alongside a portfolio that shows the skills in real work.