{"total":17,"rows":[{"id":"2103.06064","title":"Graph Neural Networks Inspired by Classical Iterative Algorithms","created":"2021-03-10","cited_by_count":60,"area":null},{"id":"2111.04840","title":"Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods","created":"2021-11-08","cited_by_count":58,"area":null},{"id":"2108.10521","title":"Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study","created":"2021-08-24","cited_by_count":36,"area":null},{"id":"2205.13891","title":"Transformers from an Optimization Perspective","created":"2022-05-27","cited_by_count":27,"area":null},{"id":"2306.09623","title":"From Hypergraph Energy Functions to Hypergraph Neural Networks","created":"2023-06-16","cited_by_count":18,"area":null},{"id":"2111.06592","title":"Implicit vs Unfolded Graph Neural Networks","created":"2021-11-12","cited_by_count":13,"area":null},{"id":"2110.13413","title":"Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features","created":"2021-10-26","cited_by_count":9,"area":null},{"id":"2206.11081","title":"Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks","created":"2022-06-22","cited_by_count":9,"area":null},{"id":"2402.05569","title":"Training-Free Message Passing for Learning on Hypergraphs","created":"2024-02-08","cited_by_count":7,"area":null},{"id":"2310.09516","title":"Efficient Link Prediction via GNN Layers Induced by Negative Sampling","created":"2023-10-14","cited_by_count":6,"area":null},{"id":"2206.04471","title":"Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective","created":"2022-06-09","cited_by_count":2,"area":null},{"id":"2210.01002","title":"ASGNN: Graph Neural Networks with Adaptive Structure","created":"2022-10-03","cited_by_count":2,"area":null},{"id":"2310.12457","title":"MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy","created":"2023-10-19","cited_by_count":2,"area":null},{"id":"2206.08473","title":"A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features","created":"2022-06-16","cited_by_count":1,"area":null},{"id":"2403.04763","title":"BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization","created":"2024-03-07","cited_by_count":1,"area":null},{"id":"2403.18142","title":"HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks","created":"2024-03-26","cited_by_count":0,"area":null},{"id":"2608.02455","title":"Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guarantees","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}