Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
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Updated
Sep 26, 2024 - Python
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
Structural Deep Clustering Network
[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering.
[AAAI 2022] An official source code for paper Deep Graph Clustering via Dual Correlation Reduction.
A pytorch implementation of the paper Unsupervised Deep Embedding for Clustering Analysis.
Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation
Papers for Open Knowledge Discovery
This project is a scalable unified framework for deep graph clustering.
AAAI 2021-Deep Fusion Clustering Network
A very simple self-supervised image classification framework!
Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of variational autoencoders to find multiple disentangled clusterings of data.
Source code for E2DTC: An End to End Deep Trajectory Clustering Framework via Self-Training. ICDE 2021.
The code of AGCN (Attention-driven Graph Clustering Network), which is accepted by ACM MM 2021.
Graph Agglomerative Clustering (GAC) toolbox
[BMVC2023] Official code for TEMI: Exploring the Limits of Deep Image Clustering using Pretrained Models
Graph Agglomerative Clustering Library
Author implementation of deep clustering model from the paper "Learning Embedding Space for Clustering From Deep Representations".
Official implementation for [N2DCX] Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
Course project for EE698R (2020-21 Sem 2). An X-Vector Based Speaker Diarization System with AutoEncoder based clustering method. Also supports spectral and KMeans clustering method.
TensorFlow implementation of the Dissimilarity Mixture Autoencoder: https://arxiv.org/abs/2006.08177
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