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perf: use incident-edge iteration in GraphSampler for faster subgraph induction - #140
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sauravbhattacharya001 merged 1 commit intoMar 30, 2026
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…e scan When building induced subgraphs from sampled nodes, buildResult and buildResultFromEdges previously iterated over ALL edges in the original graph — O(|E_original|) — to find which ones connect sampled nodes. For small sample fractions on large graphs, this is wasteful. Now iterates over incident edges of sampled nodes only, reducing edge-induction cost to O(sum of degrees of sampled nodes). For a 10% sample of a 100K-edge graph, this can be 10x faster.
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Problem
\�uildResult\ and \�uildResultFromEdges\ in \GraphSampler\ iterate over all edges in the original graph to find which ones connect sampled nodes. For small sample fractions (e.g., 10% of a large graph), this scans 100% of edges to find the ~1% that matter.
Fix
Iterate over incident edges of sampled nodes instead. Uses a \HashSet\ to avoid duplicates (each undirected edge is incident to both endpoints).
Complexity change: O(|E_original|) → O(Σ degree(v) for v in sample)
For a 10% node sample on a 100K-edge sparse graph, this can be ~10x faster.
Changes