Community Detection
227 papers with code • 14 benchmarks • 12 datasets
Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.
Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Benchmarks
These leaderboards are used to track progress in Community Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Community Detection models and implementations
Pubmed
Cora
SNAP
Placenta
Latest papers with no code
Understanding Opinions Towards Climate Change on Social Media
In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media.
Self-similarity of Communities of the ABCD Model
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes.
Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks
Under the mixture multi-layer stochastic block model (MMSBM), we show that the minimax optimal network clustering error rate, which takes an exponential form and is characterized by the Renyi divergence between the edge probability distributions of the component networks.
Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs
Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function across species.
Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations
Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time.
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
The model employs k-means as an objective function and utilizes a multi-head Graph Attention Auto-Encoder for decoding the representations.
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training
However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.
Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy
We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems.
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.
Generalized Category Discovery with Clustering Assignment Consistency
To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency.
