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
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
amoliu/CayleyNet
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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.
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 .
Community detection with spiking neural networks for neuromorphic hardware
Using a fully connected spiking neuron system, with both inhibitory and excitatory synaptic connections, the firing patterns of neurons within the same community can be distinguished from firing patterns of neurons in different communities.
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.
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.
Streaming Graph Neural Networks
alge24/DyGNN
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Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
Community Detection with Graph Neural Networks
joanbruna/GNN_community
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ICLR 2018
This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be translated in terms of learning within appropriate models.
Ensemble Clustering for Graphs: Comparisons and Applications
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering.
Position-aware Graph Neural Networks
JiaxuanYou/P-GNN
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Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
From Node Embedding To Community Embedding : A Hyperbolic Approach
drewwilimitis/hyperbolic-learning
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Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem.
