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graphs-7

Community Detection

3260 papers • 126 benchmarks • 313 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

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Benchmarks

These leaderboards are used to track progress in community-detection-7

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Amazon

Amazon

Cora

Cora

Facebook Politicians

Facebook Politicians

Libraries

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Use these libraries to find community-detection-7 models and implementations

benedekrozemberczki/karateclub

9 papers 2,115

Datasets

Pubmed

Cora

Citeseer

DBLP

SNAP

Orkut

Email-EU

Subtasks

Local Community Detection Network Community Partition Online Community Detection

Most implemented papers

Community detection in networks: A user guide

S. Fortunato, Darko Hric•Fri Jul 29 2016

A guided tour through the main aspects of community detection in networks is offered, pointing out strengths and weaknesses of popular methods, and giving directions to their use.

1992

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Facebook TV Show

Facebook TV Show

Facebook Companies

Facebook Companies

Facebook Athletes

Facebook Athletes

Facebook Media

Facebook Media

Facebook Celebrities

Facebook Celebrities

Facebook Artists

Facebook Artists

Facebook Government

Facebook Government

DBLP

DBLP

Citeseer

Citeseer

Pubmed

Pubmed

Twitter-HyDrug

Twitter-HyDrug

kfoynt/LocalGraphClustering

2 papers 135

Yeast

HLA-Chat

Placenta

0

Agglomerative likelihood clustering

Lionel Yelibi, Tim Gebbie•Thu Aug 01 2019

It is argued that ALC can reduce computation time costs and resource usage costs for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement.

1 0

Hidden Community Detection in Social Networks

Yingru Li, J. Hopcroft, Kun He, S. Soundarajan•Thu Feb 23 2017

A new graph-theoretical concept of hidden community for analysing complex networks, which contain both stronger or dominant communities and weak communities, and HICODE, which provides a promising technique to pinpoint the existing latent communities and uncover communities for which there is no ground truth.

111 0

Supervised Community Detection with Line Graph Neural Networks

Joan Bruna, Lisha Li, Zhengdao Chen•Mon May 22 2017

This work presents a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting and shows 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.

356 0

Fast Sequence-Based Embedding with Diffusion Graphs

Benedek Rozemberczki, Rik Sarkar•Sun Mar 04 2018

A graph embedding is a representation of graph vertices in a low- dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods.

68 0

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

A. Narayanan, Lihui Chen, Yang Liu, Mahinthan Chandramohan, S. Saminathan•Tue Jun 28 2016

It is demonstrated that subgraph vectors learnt by the approach could be used in conjunction with classifiers such as CNNs, SVMs and relational data clustering algorithms to achieve significantly superior accuracies on both supervised and unsupervised learning tasks.

160 0

Boosting Multitask Learning on Graphs through Higher-Order Task Affinities

Hongyang Zhang, Haotian Ju, Dongyue Li, Aneesh Sharma•Fri Jun 23 2023

This paper considers predicting multiple node labeling functions on graphs simultaneously and revisits this problem from a multitask learning perspective, developing an algorithm to cluster tasks into groups based on a higher-order task affinity measure.

15 0

Overlapping community detection at scale: a nonnegative matrix factorization approach

J. Leskovec, Jaewon Yang•Sun Feb 03 2013

This paper presents BIGCLAM (Cluster Affiliation Model for Big Networks), an overlapping community detection method that scales to large networks of millions of nodes and edges and builds on a novel observation that overlaps between communities are densely connected.

956 0

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

M. Bronstein, Federico Monti, X. Bresson, R. Levie•Sun May 21 2017

A new spectral domain convolutional architecture for deep learning on graphs with a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest.

731 0

Adding a benchmark result helps the community track progress.

Community Detection | State-of-the-Art