graph partitioning
57 papers with code • 1 benchmarks • 2 datasets
Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.
Libraries
Use these libraries to find graph partitioning models and implementations
HAM
Most implemented papers
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion.
Local Motif Clustering via (Hyper)Graph Partitioning
A widely-used operation on graphs is local clustering, i. e., extracting a well-characterized community around a seed node without the need to process the whole graph.
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
lukemelas/deep-spectral-segmentation
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CVPR 2022
We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene.
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration
tomdbar/ecord
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Compared to the nearest competitor, ECORD reduces the optimality gap by up to 73% on 500 vertex graphs with a decreased wall-clock time.
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
TheLeprechaun25/neural-improvement-heuristics
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Conducted experiments demonstrate that the proposed model can recommend neighborhood operations that outperform conventional versions for the Preference Ranking Problem with a performance in the 99th percentile.
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
Joinn99/FPSR
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In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
anshuman23/cfc
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Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.
Task-specific Scene Structure Representations
jsshin98/ssgnet
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Understanding the informative structures of scenes is essential for low-level vision tasks.
Approximate spectral clustering density-based similarity for noisy datasets
Also, CONN could be tricked by noisy density between clusters.
Approximate spectral clustering with eigenvector selection and self-tuned $k$
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption.
