Node Clustering
62 papers with code • 19 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Node Clustering
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Libraries
Use these libraries to find Node Clustering models and implementationsDatasets
IMDb Movie Reviews
Pubmed
Cora
Wiki Squirrel
Wiki-CS
Latest papers with no code
Unsupervised Optimisation of GNNs for Node Clustering
Although modularity is a graph partitioning quality metric, we show that this can be used to optimise GNNs that also encode features without a drop in performance.
Community Detection and Classification Guarantees Using Embeddings Learned by Node2Vec
Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available.
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
Universal Graph Random Features
This includes many of the most popular examples of kernels defined on the nodes of a graph.
Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More
However, graphs often also exhibit heterophilous structure, as exemplified by (nearly) bipartite and tripartite graphs, where most edges occur across the clusters.
Curvature-based Clustering on Graphs
We consider several discrete curvature notions and analyze the utility of the resulting algorithms.
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs).
CARL-G: Clustering-Accelerated Representation Learning on Graphs
CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline.
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
It makes evaluating models for large-scale temporal graph clustering challenging.
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels.
