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Found 9 papers, 4 papers with code
Leaping Through Time with Gradient-based Adaptation for Recommendation
Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies.
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Theoretical analyses for graph learning methods often assume a complete observation of the input graph.
Adaptive Stacked Graph Filter
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully-connected weights versus trainable polynomial coefficients.
Stacked Graph Filter
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus trainable polynomial coefficients.
Graph Homomorphism Convolution
In this paper, we study the graph classification problem from the graph homomorphism perspective.
A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks
We present a simple proof for the universality of invariant and equivariant tensorized graph neural networks.
Frequency Analysis for Graph Convolution Network
In this work, we develop quantitative results to the learnablity of a two-layers Graph Convolutional Network (GCN).
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
However, we find that the feature vectors of benchmark datasets are already quite informative for the classification task, and the graph structure only provides a means to denoise the data.
Learning Graph Neural Networks with Noisy Labels
We study the robustness to symmetric label noise of GNNs training procedures.
