Abstract:Bipartite knowledge graphs in niche domains are typically data-poor and edge-sparse, which hinders link prediction. We introduce AEGIS (Authentic Edge Growth In Sparsity), an edge-only augmentation framework that resamples existing training edges -either uniformly simple or with inverse-degree bias degree-aware -thereby preserving the original node set and sidestepping fabricated endpoints. To probe authenticity across regimes, we consider naturally sparse graphs (game design pattern's game-pattern network) and induce sparsity in denser benchmarks (Amazon, MovieLens) via high-rate bond percolation. We evaluate augmentations on two complementary metrics: AUC-ROC (higher is better) and the Brier score (lower is better), using two-tailed paired t-tests against sparse baselines. On Amazon and MovieLens, copy-based AEGIS variants match the baseline while the semantic KNN augmentation is the only method that restores AUC and calibration; random and synthetic edges remain detrimental. On the text-rich GDP graph, semantic KNN achieves the largest AUC improvement and Brier score reduction, and simple also lowers the Brier score relative to the sparse control. These findings position authenticity-constrained resampling as a data-efficient strategy for sparse bipartite link prediction, with semantic augmentation providing an additional boost when informative node descriptions are available.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.22017 [cs.LG] |
| (or arXiv:2509.22017v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2509.22017 arXiv-issued DOI via DataCite |
From: Hugh Xuechen Liu [view email]
[v1]
Fri, 26 Sep 2025 07:51:40 UTC (48 KB)
[v2]
Sun, 30 Nov 2025 18:46:57 UTC (12,755 KB)
[v3]
Sun, 8 Mar 2026 11:24:25 UTC (12,755 KB)
[v4]
Thu, 30 Apr 2026 06:28:31 UTC (12,756 KB)
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