It is shown that private graph calibration can be framed as a structural problem: choosing a calibration set that is large enough to provide useful calibration, yet sparse enough that the privacy noise remains manageable, and it is formalized that it is NP-hard.
HeAD-CP is proposed, a family of node-wise diffusion variants whose coefficients are determined by a label-free local-homophily estimate derived from the GNN softmax, which are most effective at extreme heterophily, intermediate heterophily, and moderate-to-high homophily, respectively, and all preserve the marginal co...
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observ...
Purui Zhang, Feng Ji, Yanan Zhao et al.· arXiv.org· 0 citations
Conformal prediction (CP) provides distribution-free coverage guarantees and has emerged as a principled tool for uncertainty quantification. In edge-level fraud detection on temporal interaction graphs, where false positives and false negatives both carry substantial cost, such coverage guarantees are particularly app...
Xudong Chen, Shengbo Gong, Lu Cheng et al.· Proceedings of the 32nd ACM...· 0 citations
This work develops OCP with queries (OCPQ) by adapting the label efficient forecaster of Cesa-Bianchi, Lugosi, and Stoltz (2004) to the authors' setting, and develops OCP with queries (OCPQ) with queries in a way that encourages the learner to output small prediction sets while ensuring that the correct label is covere...
J. Skalse, Edoardo Pona, Osvaldo Simeone et al.· 0 citations
Graph neural networks learn from relational structure but can be sensitive to edge-level noise. We study a hybrid graph classifier that combines a message-passing branch (Graph Isomorphism Network, GIN) with a topological branch based on extended persistence diagrams and PersLay embeddings. Training optionally uses a p...
Jelena Losic, Charles Fanning· PLOS Complex Systems· 0 citations
Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal calibration split. We study the separate efficiency problem: can pruning preserve score geometry well enough to obtain smaller valid prediction sets? Calibration-Preserv...
