# Hybrid Low-Order and Higher-Order Graph Convolutional Networks

@article{Lei2020HybridLA, title={Hybrid Low-Order and Higher-Order Graph Convolutional Networks}, author={Fangyuan Lei and Xun Liu and Qingyun Dai and Bingo Wing-Kuen Ling and Huimin Zhao and Yan Liu}, journal={Computational Intelligence and Neuroscience}, year={2020}, volume={2020} }

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the…

## 11 Citations

MulStepNET: stronger multi-step graph convolutional networks via multi-power adjacency matrix combination

- Computer ScienceJournal of Ambient Intelligence and Humanized Computing
- 2021

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- Computer Science
- 2021

This paper adopts the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting and designs a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops.

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- Computer ScienceComplex & Intelligent Systems
- 2021

AdjMix is proposed, a simple and attentional graph convolutional model that is scalable to wider structure and captures more nodes features information, by simultaneously mixing the adjacency matrices of different powers.

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- Computer ScienceSDM
- 2020

This paper proposes SMOG-CF (Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering), a GCN-based framework that can directly capture high-order connectivity among nodes and facilitates ‘path-level’ information propagation between neighboring nodes at any order.

Hop-Aware Dimension Optimization for Graph Neural Networks

- Computer ScienceArXiv
- 2021

A simple yet effective ladder-style GNN architecture, namely LADDER-GNN, that separate messages from different hops and assign different dimensions for them before concatenating them to obtain the node representation.

Ladder-GNN: Hop-Aware Representation Learning for Graph Neural Networks

- Computer Science
- 2021

A simple yet effective hopaware aggregation scheme is proposed, resulting in a ladder-style GNN architecture, namely Ladder-GNN, which verifies the proposed LadderGNN on seven semi-supervised node classification datasets, including both homogeneous and heterogeneous graphs.

Relational Graph Neural Network Design via Progressive Neural Architecture Search

- Computer Science
- 2021

We propose a novel solution to addressing a longstanding dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded…

Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification

- Education, Materials Science
- 2021

Guangdong Key Provincial Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China School of Electronic and Information, Guangdong Polytechnic…

Research on autonomous underwater vehicle wall following based on reinforcement learning and multi-sonar weighted round robin mode

- Engineering
- 2020

A novel work mode with weighted polling which can independently identify the environment, dynamically establish the environmental model, and switch the operating frequency of the sonar is proposed and the tank test verified the effectiveness of the algorithm.

Graph neural network with feature enhancement of isolated marginal groups

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- 2022

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