Does Graph Compression Preserve Signal Propagation?
Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work evaluates compression through downstream task performance or structural preservation, neither of which directly captures how propagation dynamics change after compression. We study two fundamental compression paradigms, coarsening and sparsification, and ask whether they preserve the propagation behavior of the original graph. Across five datasets, varying compression rates, and propagation depths, we measure signal behavior through three complementary metrics. Our results reveal a consistent tension between the two compression families. Spa...
| Authors | Kawshik Banerjee, Khaled Mohammed Saifuddin |
|---|---|
| Primary category | cs.LG |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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