A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

Last update: Apr 29, 2022

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Overview

CCasGNN

A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

Dataset

We publish the example of processed dataset. Each "list" represents the cascade information. It contains edges, features, nodes(the order of diffusion)

Downlowd link: https://drive.google.com/drive/folders/13k2yKJhRSgGOP9GSBJG7aTsS90JwG337?usp=sharing

The original dataset can be download at: https://www.aminer.cn/data-sna#Twitter-Dynamic-Net, https://www.aminer.cn/citation

Owner

GitHub Repository

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A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

Related tags

Overview

CCasGNN

Dataset

Owner

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

Taming Transformers for High-Resolution Image Synthesis

The object detection pipeline is based on Ultralytics YOLOv5

Puzzle-CAM: Improved localization via matching partial and full features.

(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Python Implementation of Chess Playing AI with variable difficulty

Stock-history-display - something like a easy yearly review for your stock performance

This is a repository of our model for weakly-supervised video dense anticipation.

Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

Official Implementation of SWAD (NeurIPS 2021)

Deep learning toolbox based on PyTorch for hyperspectral data classification.

Generating Images with Recurrent Adversarial Networks

Anti-UAV base on PaddleDetection

This Deep Learning Model Predicts that from which disease you are suffering.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN)

Energy consumption estimation utilities for Jetson-based platforms

商品推荐系统

A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

Related tags

Overview

CCasGNN

Dataset

Owner

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

Taming Transformers for High-Resolution Image Synthesis

The object detection pipeline is based on Ultralytics YOLOv5

Puzzle-CAM: Improved localization via matching partial and full features.

(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Python Implementation of Chess Playing AI with variable difficulty

Stock-history-display - something like a easy yearly review for your stock performance

This is a repository of our model for weakly-supervised video dense anticipation.

Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

Official Implementation of SWAD (NeurIPS 2021)

Deep learning toolbox based on PyTorch for hyperspectral data classification.

Generating Images with Recurrent Adversarial Networks

Anti-UAV base on PaddleDetection

This Deep Learning Model Predicts that from which disease you are suffering.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN)

Energy consumption estimation utilities for Jetson-based platforms

商品推荐系统

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang