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

MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice

Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

3D-Transformer: Molecular Representation with Transformer in 3D Space

Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis

Landmarks Recogntion Web application using Streamlit.

A machine learning project which can detect and predict the skin disease through image recognition.

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

Implementation of ToeplitzLDA for spatiotemporal stationary time series data.

This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.

I3-master-layout - Simple master and stack layout script

A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.

Classification Modeling: Probability of Default

SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.

Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity

Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

Hard cater examples from Hopper ICLR paper

Library for 8-bit optimizers and quantization routines.

[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation