Link Prediction Lab: Node Embeddings & Edge Scoring

Aggregation

Neighbor mix α 40%

Link Scoring

Method

Score threshold 60%

Held-out test edges 20%

Live Stats

AUC (test)

—

Iterations

Predicted correct

0 / 0

False positives

Train edge (seen)

Held-out link — predicted

Held-out link — missed

False-positive prediction

Node = community (hidden from model)

How it works

Each node starts with a random 3D embedding vector. A GCN-style mean-aggregation rule repeatedly mixes every node's embedding with the average of its train-graph neighbors:

e_i ← (1-α)·e_i + α · mean_{j ∈ N(i)} e_j
e_i ← e_i / ‖e_i‖  (renormalized to a fixed radius)

This is the same neighbor-averaging step at the heart of GCN, GraphSAGE and label propagation — after enough steps, embeddings of nodes that share many neighbors converge, even if they were never directly connected.

A held-out fraction of real edges is hidden from the aggregation step entirely (a realistic train/test split). Link prediction then scores every non-train pair with:

cosine(i,j) = (e_i · e_j) / (‖e_i‖ ‖e_j‖)
dot(i,j)    = e_i · e_j

A pair scores above the threshold → predicted link. AUC estimates P(score(held-out link) > score(random non-link)) over all test pairs — 50% is random guessing, 100% is a perfect ranking.

α slider — how strongly each step pulls a node toward its neighbors' average; α=0 freezes embeddings, high α converges fast but can overshoot.
Step / Auto — apply one aggregation round, or run continuously.
Threshold — the score cutoff for "predicted link"; raising it trades recall for precision.
Held-out % — how many real edges are hidden from the model before it aggregates, then used only to grade its predictions.

16 nodes · 3 communities

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