Graph Clustering
146 papers with code • 10 benchmarks • 18 datasets
Graph Clustering is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.
Libraries
Use these libraries to find Graph Clustering models and implementationsDatasets
Most implemented papers
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks.
Graph-Bert: Only Attention is Needed for Learning Graph Representations
We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
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.
Laplacian Regularized Few-Shot Learning
Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.
Laplacian Regularized Few-Shot Learning
We propose a transductive Laplacian-regularized inference for few-shot tasks.
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.
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.