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Found 7 papers, 4 papers with code
Dynamic Graph Representation Learning with Neural Networks: A Survey
In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years.
Technical report: Graph Neural Networks go Grammatical
This paper introduces a framework for formally establishing a connection between a portion of an algebraic language and a Graph Neural Network (GNN).
Breaking the Limits of Message Passing Graph Neural Networks
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemented and still raise a lot of interest even though their theoretical expressive power is limited to the first order Weisfeiler-Lehman test (1-WL).
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective
Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence in polynomial time, the theoretical power of GNNs is usually evaluated by the equivalence of WL-test order, followed by an empirical analysis of the models on some reference inductive and transductive datasets.
Neural Networks Regularization Through Class-wise Invariant Representation Learning
In this work, we tackle the issue of training neural networks for classification task when few training samples are available.
Graph edit distance : a new binary linear programming formulation
Moreover, a relaxation of the domain constraints in the formulations provides efficient lower bound approximations of the GED.
Deep Neural Networks Regularization for Structured Output Prediction
The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.
