Transfer learning based graph convolutional network with self ...

In this paper, we propose a transfer learning based graph convolutional network with self-attention mechanism method to detect abnormal electricity consumption.

Transfer Learning Based Graph Convolutional Network with Self ...

Transfer Learning Based Graph Convolutional Network with Self-Attention Mechanism for Abnormal Electricity Consumption Detection. 37 Pages Posted: 27 Dec ...

Transfer Learning based Graph Convolutional Network with Self ...

reduces the training burden. 1.3. The main contents and contributions. In this paper, the transfer learning based graph convolutional network(TLSA-GCN) is.

Transfer learning based graph convolutional network with self ...

Request PDF | On Dec 1, 2023, Songping Meng and others published Transfer learning based graph convolutional network with self-attention mechanism for ...

Transfer Learning Based Graph Convolutional Network with Self ...

Request PDF | On Jan 1, 2022, Songping Meng and others published Transfer Learning Based Graph Convolutional Network with Self-Attention Mechanism for ...

Transfer learning based graph convolutional network with self ...

Transfer learning based graph convolutional network with self-attention mechanism for abnormal electricity consumption detection · List of references.

Graph convolutional networks with the self-attention mechanism for ...

[27] presented a graph-based transfer learning approach. The authors extracted features of nodes to train the GLSTM model from the local, ...

Transfer learning with graph neural networks for improved molecular ...

The design of the readout functions is a fundamental aspect of geometric deep learning, and a transition to neural network-based operators, also ...

LirongWu/awesome-graph-self-supervised-learning - GitHub

EGI: Transfer Learning of Graph Neural Networks with Ego-graph ... Node Attribute and Embedding Denoising: Graph-based Neural Network Models with Multiple Self- ...

A comparison review of transfer learning and self-supervised learning

Cao L., Xiang W. Application of convolutional neural network based on transfer learning for garbage classification. 2020 IEEE 5th information ...

Transfer Learning of Graph Neural Networks with Ego ... - NIPS papers

This view motivates us to design EGI, a novel GNN training objective based on ego-graph information maximization, which is effective in capturing the graph ...

Attention is all you need for boosting graph convolutional neural ...

GraphSAGE [11] leverages self-supervised learning to ... Prasanna, “Graphsaint: Graph sampling based inductive learning method,” Jul 2019.

MRI reconstruction with enhanced self-similarity using graph ... - NCBI

Transfer learning in deep neural network based under-sampled MR image reconstruction. Magn Reson Imaging. 2021;76:96–107. doi: 10.1016/j.mri ...

Transfer Learning of Graph Neural Networks with Ego-graph ...

This view motivates us to design EGI, a novel GNN training objective based on ego-graph information maximization, which is effective in capturing the graph ...

CogTrans: A Cognitive Transfer Learning-based Self-Attention ...

... Graph (KG), which infers new knowledge based on existing knowledge. Especially, the Graph Convolution Network (GCN)-based approaches can obtain state-of-the ...

Self-Supervised Learning of Graph Neural Networks: A Unified Review

[20] train CNNs to capture dependencies between different augmentations of an image. Based on how the pretext training tasks are designed,. SSL methods can be ...

AAGCN: a graph convolutional neural network with adaptive feature ...

GCN: This method is a deep learning model based on graph ... Transfer learning with graph neural networks for short-term highway ...

Transfer Learning with Graph Attention Networks for Team ...

In this paper, we propose a new architecture, LANT, which comprises transfer learning and neural team recommendation, to address these challenges based on ...

Transfer Learning With Graph Neural Networks | Restackio

In contrast, spatial-based approaches, such as Graph Attention Networks (GATs) and GraphSAGE, focus on the local neighborhood of nodes ...

STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS

and self-supervised methods for pre-training Graph Neural Networks (GNNs). ... Here, we focus on pre-training as an approach to transfer learning in Graph Neural.