Graphlet- and motif-based link prediction in large networks

Link prediction is important across biological, social, and technological networks, but many methods either require domain-specific node attributes, do not scale to large graphs, or miss higher-order topology. We present BLANT-Predict, a topology-only framework that uses sampled graphlets and orbit-pair frequencies to rank likely missing edges. Across 12 real-world networks (up to about 1 million nodes and 3 million edges), we compare against 13 baseline methods and observe higher precision with strong scalability. Beyond standard k-fold cross-validation, we evaluate predictions against future out-of-sample network snapshots to better reflect real deployment. BLANT-Predict maintains superior precision in these future tests, indicating practical value for predicting previously unobserved links.