1. GNN-About-Medical-Treatment ：图神经网络（医疗健康方向）
1.Disease prediction：疾病监测
1.1Distance Metric Learning using Graph Convolutional Networks : Application to Functional Brain Networks
1.2Spectral Graph Convolutions for Population-based Disease Prediction
1.3Convolutional neural networks for mesh-based parcellation of the cerebral cortex
1.4Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease
1.5Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification
1.6Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
2.Imaging：成像检查
2.1EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Network and Broad Learning System
2.23D fully convolutional networks for co-segmentation of tumors on PET-C images
2.3EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Network
2. 3D Inception：3D Inception（医疗健康方向）
1.Classification of Pancreatic Tumors based on MRI Images using 3D Convolutional Neural Networks
2.3D Inception Convolutional Neural Networks For Automatic Lung Nodule Detection
3.3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer’s Disease diagnostics
3. Inception v1-4：Inception（卷积神经网络模型）Google大佬们的优化历程
1.Inception[V1]: Going Deeper with Convolutions
2.Inception[V2]: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
3.Inception[V3]: Rethinking the Inception Architecture for Computer Vision
4.Inception[V4]: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
4. Convolutional Neural Networks for Sentence Classification：基于CNN实现文本分类
5. GraphTech-And-GNN-About-Unsupervised-Learning：基于图技术和图神经网络的无监督学习算法
1.Community Detection - Common Algorithm
2.Related DataSet
3.Spectral Clustering
4.GCN/GAE
6. Paper-Robot：自动生成新论文
7. Financial-Time-Series-Prediction：金融时间序列预测
1.A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction
2.Applied attention-based LSTM neural networks in stock prediction
3.Comparison of BPA-MLP and LSTM-RNN for Stocks Prediction
4.Stock Correlation Analysis Based on Complex Network
5.Stock price forecast using wavelet ransformations in multiple time windows and neural network
6.Stock Transaction Prediction Modeling and Analysis Based on LSTM
7.Stocks Analysis and Prediction Using Big Data Analytics
8.Stock Market Movement Prediction using LDA-Online Learning Model
9.Classification Models for Stock’s Performance Prediction A Case Study of Finance Sector in the Thailand
10.Stock Price Prediction Using Attention-based Multi-Input LSTM
8. Wide & Deep Learning for Recommender Systems：Google Player 推荐系统