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Top Papers in Graph neural networks

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Continuous Graph Neural Networks

This paper builds on the connection between graph neural networks and
traditional dynamical systems. We propose continuous graph neural networks
(CGNN), which generalise existing graph neural networks

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Optimal Transport Graph Neural Networks

Current graph neural network (GNN) architectures naively average or sum node
embeddings into an aggregated graph representation -- potentially losing
structural or semantic information. We here introd

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A Survey on Graph Neural Networks

Graph Neural Networks: Taxonomy, Advances and Trends

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Locally Private Graph Neural Networks

Graph Neural Networks (GNNs) have demonstrated superior performance in
learning node representations for various graph inference tasks. However,
learning over graph data can raise privacy concerns whe

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Spatio-Temporal Graph Neural Networks: A Survey

Graph Neural Networks have gained huge interest in the past few years. These
powerful algorithms expanded deep learning models to non-Euclidean space and
were able to achieve state of art performance

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Graph Neural Networks in Wireless Networks

An Overview on the Application of Graph Neural Networks in Wireless Networks

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Should Graph Neural Networks Use Features, Edges, Or Both?

Graph Neural Networks (GNNs) are the first choice for learning algorithms on
graph data. GNNs promise to integrate (i) node features as well as (ii) edge
information in an end-to-end learning algorith

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Quantum Graph Neural Networks

We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum
neural network ansatze which are tailored to represent quantum processes which
have a graph structure, and are particularly su

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Implicit Graph Neural Networks

Implicit Graph Neural Networks

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Benchmarking Graph Neural Networks

Graph neural networks (GNNs) have become the standard toolkit for analyzing
and learning from data on graphs. As the field grows, it becomes critical to
identify key architectures and validate new ide

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Customized Graph Neural Networks

Recently, Graph Neural Networks (GNNs) have greatly advanced the task of
graph classification. Typically, we first build a unified GNN model with graphs
in a given training set and then use this unifi

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Elastic Graph Neural Networks

While many existing graph neural networks (GNNs) have been proven to perform
$\ell_2$-based graph smoothing that enforces smoothness globally, in this work
we aim to further enhance the local smoothne

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Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling graph
learning tasks, yet have been shown to be oblivious to eminent substructures,
such as cycles. We present TOGL, a novel layer

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Binarization of Graph Neural Networks

Binary Graph Neural Networks

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Hierarchical Graph Neural Networks

Over the recent years, Graph Neural Networks have become increasingly popular
in network analytic and beyond. With that, their architecture noticeable
diverges from the classical multi-layered hierarc

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Quaternion Graph Neural Networks

We consider reducing model parameters and moving beyond the Euclidean space to a hyper-complex space in graph neural networks (GNNs). To this end, we utilize the Quaternion space to learn quaternion n

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Nested Graph Neural Networks

Graph neural network (GNN)'s success in graph classification is closely
related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating
neighboring node features to a center node, both 1

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Constant Time Graph Neural Networks

The recent advancements in graph neural networks (GNNs) have led to
state-of-the-art performances in various applications, including
chemo-informatics, question-answering systems, and recommender syst

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A Comprehensive Survey on Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent years,
ranging from image classification and video processing to speech recognition
and natural language understanding. The data

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Hessian Bounds for Graph Neural Networks

Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion

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