Community Detection
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Subtasks
Greatest papers with code
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.
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 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
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.
SOTA for Community Detection on Cora
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.
SOTA for Node Classification on Wiki
LOCAL COMMUNITY DETECTION NETWORK COMMUNITY PARTITION NODE CLASSIFICATION REPRESENTATION LEARNING
SINE: Scalable Incomplete Network Embedding
Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.
COMMUNITY DETECTION LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
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 .
SOTA for Community Detection on Amazon (NMI metric )
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.