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
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Libraries
Use these libraries to find Community Detection models and implementations
Pubmed
Cora
SNAP
Placenta
Most implemented papers
Single Image Reflection Removal through Cascaded Refinement
IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and information across steps of the cascade is transferred using an LSTM.
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power-law degree distribution, and other typical properties observed in complex networks.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
Possible reasons for this are: the steep learning curve for these algorithms; the lack of efficient and easy to use software; and the lack of detailed numerical experiments on real-world data that demonstrate their usefulness.
$p$-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
Amortized Probabilistic Detection of Communities in Graphs
aripakman/attentive_clustering_processes
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While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.
Generative model for reciprocity and community detection in networks
Inference is performed using an efficient expectation-maximization algorithm that exploits the sparsity of the network, leading to an efficient and scalable implementation.
Synwalk -- Community Detection via Random Walk Modelling
We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalk's performance with the performance of Infomap and Walktrap.
Generative hypergraph clustering: from blockmodels to modularity
Many graph algorithms for this task are based on variants of the stochastic blockmodel, a random graph with flexible cluster structure.
Evaluating Node Embeddings of Complex Networks
Graph embedding is a transformation of nodes of a graph into a set of vectors.
