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.
Benchmarks
These leaderboards are used to track progress in Graph Clustering
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Libraries
Use these libraries to find Graph Clustering models and implementationsDatasets
Pubmed
Cora
SNAP
Netzschleuder
97 synthetic datasets
Placenta
Latest papers with no code
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
However, most existing graph clustering methods focus on node-level clustering, i. e., grouping nodes in a single graph into clusters.
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface.
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training
However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.
Local Graph Clustering with Noisy Labels
We provide sufficient conditions on the label noise under which, with high probability, using diffusion in the weighted graph yields a more accurate recovery of the target cluster.
The Map Equation Goes Neural
We consider the map equation, an information-theoretic objective function for unsupervised community detection.
Clustering Without an Eigengap
Our gap-free clustering procedure also leads to improved algorithms for recursive clustering.
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping.
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
It makes evaluating models for large-scale temporal graph clustering challenging.
G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).
