Problem
Research questions and friction points this paper is trying to address.
Network topology inference
Graph learning
Heterogeneous sources
Wasserstein metric
Distributionally robust
Multi-Source Wasserstein Distributionally Robust Graph Learning
🤖 AI Summary
该研究针对图信号处理中的网络拓扑推断问题，提出了一种基于Wasserstein分布鲁棒性的多源图学习框架MS-WDRO，通过融合异构数据源来提高图结构的恢复精度。
📝 Abstract
Network topology inference from graph signals is central to graph signal processing with applications in neuroscience, sensor, and social networks. In practice, target-domain samples are scarce while heterogeneous source-domain data are abundant. Fusing these sources is challenging: Euclidean averaging works for homogeneous sources but degrades sharply as inter-source divergence grows, collapsing distinct geometries into an inflated, biased consensus. We exploit the Wasserstein metric's distribution-preserving properties to counter heterogeneity while preserving each source's intrinsic geometry. We propose MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via their weighted Wasserstein barycenter, a geometrically principled nominal distribution, then builds an ambiguity ball around it to hedge residual uncertainty. Minimizing worst-case risk yields a tractable regularized Laplacian estimator solved efficiently via a provably convergent ADMM scheme. We establish non-asymptotic guarantees: a finite-sample concentration bound for the empirical barycenter, a pooling bias lower bound proving naive aggregation is suboptimal, and an out-of-sample excess risk bound decaying at a parametric rate with only logarithmic dependence on source count. To calibrate hyperparameters governing robustness, sparsity, and source fusion, we unroll the solver into a differentiable architecture trained end-to-end, achieving data-adaptive calibration beyond cross-validation while retaining interpretability. Experiments on synthetic benchmarks and the multi-site ABIDE~I neuroimaging dataset show MS-WDRO consistently outperforms seven baselines in graph recovery, sample efficiency, and downstream diagnostic utility, with the largest gains in the sample-scarce regime.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Source Wasserstein Distributionally Robust Graph Learning
Weighted Wasserstein Barycenter
Ambiguity Ball
Non-Asymptotic Guarantees
End-to-End Differentiable Architecture
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
