About
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
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Subtasks
Datasets
Greatest papers with code
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.
COMMUNITY DETECTION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MINING NODE CLASSIFICATION
Fast Sequence-Based Embedding with Diffusion Graphs
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection
Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.
COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.
Ranked #1 on
Node Classification
on Wiki
LOCAL COMMUNITY DETECTION NETWORK COMMUNITY PARTITION NODE CLASSIFICATION REPRESENTATION LEARNING
Fast Sequence Based Embedding with Diffusion Graphs
A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes.
COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION
Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters
More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase .
Ranked #2 on
Community Detection
on Amazon
Font Size: Community Preserving Network Embedding
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.
High Quality, Scalable and Parallel Community Detectionfor Large Real Graphs
However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.
Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach
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.
Sampling Community Structure
We propose a novel method, based on concepts from expander graphs, to sample communities in networks.