Community Detection

237 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


Use these libraries to find Community Detection models and implementations

Most implemented papers

Community detection in networks: A user guide

learn-co-curriculum/dsc-3-28-12-graph-connectivity-community-detection 30 Jul 2016

Community detection in networks is one of the most popular topics of modern network science.

Agglomerative Likelihood Clustering

tehraio/potts-model-clustering 2 Aug 2019

We consider the problem of fast time-series data clustering.

Hidden Community Detection in Social Networks

KunHe2015/HiCode 24 Feb 2017

We introduce a new paradigm that is important for community detection in the realm of network analysis.

Supervised Community Detection with Line Graph Neural Networks

zhengdao-chen/GNN4CD ICLR 2019

We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold.

Fast Sequence-Based Embedding with Diffusion Graphs

benedekrozemberczki/diff2vec 21 Jan 2020

A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.

subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs

mldroid/subgraph2vec_gensim 29 Jun 2016

Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.

Boosting Multitask Learning on Graphs through Higher-Order Task Affinities

virtuosoresearch/boosting-multitask-learning-on-graphs 24 Jun 2023

Lastly, we provide a theoretical analysis to show that under a planted block model of tasks on graphs, our affinity scores can provably separate tasks into groups.

Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach

benedekrozemberczki/karateclub WSDM 2013

In this paper, we develop a model-based community detection algorithm that can detect densely overlapping, hierarchically nested as well as non-overlapping communities in massive networks.

Font Size: Community Preserving Network Embedding

benedekrozemberczki/karateclub AAAI 2017

While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

amoliu/CayleyNet 22 May 2017

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.