201 papers with code • 11 benchmarks • 9 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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Most implemented papers
Community detection in networks: A user guide
Community detection in networks is one of the most popular topics of modern network science.
Agglomerative Likelihood Clustering
We consider the problem of fast time-series data clustering.
Hidden Community Detection in Social Networks
We introduce a new paradigm that is important for community detection in the realm of network analysis.
Supervised Community Detection with Line Graph Neural Networks
We show 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, which is believed to reach the computational threshold.
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.
Density-based Community Detection/Optimization
Additionally, the results of the community detection algorithm turned out to be similar to the benchmark algorithm we used.
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.
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.
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.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
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.