Ranking 315K GNN Designs with Anchor Models | Stanford CS224W Lecture 19

TL;DRThe Fall 2025 conclusion studies roughly 315K GNN designs across 32 tasks: run a small set of anchor models, derive task similarity from rankings, and transfer the best designs from similar tasks.

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This is Lecture 19 of Stanford CS224W: Machine Learning with Graphs, Fall 2025, dated 2025-12-04. It follows the course schedule and the official Lecture 19 Conclusion deck; speaker attribution follows the slides.

Materials and gaps

Public materials include the official slides and optional readings on the schedule. Canvas video, live Q&A, board work, and Ed discussions are unavailable and are not reconstructed. The public 2021 videos are not evidence for a 2025 lecture.

Complete lecture agenda

The actual conclusion question

The official Lecture 19 deck studies GNN model selection over roughly 315K designs and 32 tasks. The best design varies by task, so one fixed leaderboard is insufficient.

Step 1: randomly evaluate 100 designs on a small dataset

The official procedure first samples and trains 100 random designs on a small dataset to obtain a performance spectrum. It does not predict rankings for unevaluated designs.

Step 2: uniformly select 12 anchors across the spectrum

It then selects 12 anchor models uniformly across that performance spectrum. Covering weak, middle, and strong performance makes the anchor set informative about task behavior.

Step 3: represent each task by its anchor ranking

The same anchors are evaluated on each task. Their relative ranking becomes a behavior-based task representation rather than a similarity guessed from dataset names.

Step 4: define task similarity from anchor rankings

Task similarity is determined by anchor-model rankings. Similar anchor orderings indicate a potentially useful source task.

Step 5: transfer the best designs from a similar task

The best designs already observed on the similar source task are transferred and evaluated on the target. The deck does not propose predicting a complete ranking of all unevaluated designs or a top-k ranking model.

Controlled random search as prior methodology

The deck first presents controlled random search (CRS) as an earlier design-space search method, then develops anchor-based task similarity and best-design transfer. CRS belongs to the methodological context; the 0.771 in the OGB result is explicitly labeled Previous SOTA and must not be attributed to CRS.

OGB benchmark results

In the deck's OGB example, transferring from a similar task reaches 0.785, compared with the deck's Previous SOTA of 0.771. Transfer from a dissimilar task falls to 0.736. The 0.771 result is not a CRS score.

Reading the three numbers together

The 0.785-versus-Previous-SOTA-0.771 comparison and the 0.736 dissimilar-task negative control are distinct evidence. Calling 0.771 a CRS result, or quoting only 0.785, would misstate the deck's mechanism.

Boundaries of 315K and 32

315K is the official design-space size and 32 is the task count. This article does not claim to have rerun that complete experiment or turn it into a production guarantee.

A faithful miniature reproduction

Randomly evaluate 100 designs, uniformly select 12 anchors over their performance range, run those anchors across tasks, choose a source by ranking similarity, transfer its best designs, and record both similar-task and dissimilar-task transfer results.

Artifacts required for reproduction

Save the sampled 100 designs, their performance, the 12 anchor positions, per-task anchor rankings, task similarities, transferred source designs, and both similar-task and dissimilar-task target results.

The final workflow

Use a small-dataset performance spectrum to select anchors, use anchor rankings to identify similar tasks, and transfer known best designs from those tasks. This is a budgeted model-transfer procedure, not a universal ranking predictor.

Course video sources

Checked live: the archived Fall 2025 official page lists no public recording link (its Canvas entry is empty), and the current Fall 2026 official page says lecture videos are on Canvas for enrolled students only; the Canvas entry redirects to the Stanford sign-in page. The public Stanford Online YouTube playlists currently cover only the 2021 and 2023 offerings, whose lectures differ from the Fall 2025 offering this article follows, so they are not embedded or treated as Fall 2025 recordings.

Course and recording entries:

Checked: 2026-10-10.

Update Log

2026-10-10: Added explicit video status and checked recording sources and access notes.
2026-10-10: Rechecked video status. Confirmed the official pages list no public Fall 2025 recording, Canvas requires sign-in, and public playlists cover only 2021/2023; status unchanged and check date added.