Papers graph partitioning
“graph partitioning” 태그가 달린 논문 230편 · 필터 해제
Diversity-aware View Partitioning for Scalable VGGT
Geometry transformers such as VGGT achieve strong performance by jointly reasoning over multiple views with global attention. However, scaling them to large view collections remains challenging due to the quadratic cost …
Camera Pose Estimationgraph partitioning3D ReconstructionWhen Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding
Multi-agent Large Language Model (LLM) systems offer a way to decompose complex tasks, such as coding, through parallelization and context isolation. However, adding agents in practice introduces inter-agent communicatio…
Community Detectiongraph partitioningNeural Acceleration for Graph Partitioning
Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more. Spectral methods are known to produce quality partitions while minimi…
graph partitioningNeural QAOA$^{2}$: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization
The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-and-conquer frameworks like QAOA$^{2}$ address scalability by partitio…
Zero-shot Generalizationgraph partitioningStructural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to interpret as structural $\&$ evolutionary si…
Protein Function PredictionProtein Language Modelgraph partitioningEmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
Graph Neural Networks (GNNs) are widely used for learning on graph-structured data, but scaling GNN training to massive graphs remains challenging. To enable scalable distributed training, graphs are divided into smaller…
Graph Neural Networkgraph partitioningCausal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer
This paper tackles a critical bottleneck in Super-Structure-based divide-and-conquer causal discovery: the high computational cost of constructing accurate Super-Structures--particularly when conditional independence (CI…
graph partitioningSPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-derived affinities. Such approaches, based …
Semantic Segmentationgraph partitioningParallelizing Node-Level Explainability in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated remarkable performance in a wide range of tasks, such as node classification, link prediction, and graph classification, by exploiting the structural information in graph-st…
Graph ClassificationNode Classificationgraph partitioningLink PredictionAn Improved and Generalised Analysis for Spectral Clustering
We revisit the theoretical performances of Spectral Clustering, a classical algorithm for graph partitioning that relies on the eigenvectors of a matrix representation of the graph. Informally, we show that Spectral Clus…
graph partitioningLength-MAX Tokenizer for Language Models
We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference. Our …
graph partitioningGROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gained popularity as a potential solution to…
graph partitioningGraph LearningAre Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transformers aim to address this by enabling long…
Computational Efficiencygraph partitioningBeyond Spectral Clustering: Probabilistic Cuts for Differentiable Graph Partitioning
Probabilistic relaxations of graph cuts offer a differentiable alternative to spectral clustering, enabling end-to-end and online learning without eigendecompositions, yet prior work centered on RatioCut and lacked gener…
Contrastive Learninggraph partitioningSpEx: A Spectral Approach to Explainable Clustering
Explainable clustering by axis-aligned decision trees was introduced by Moshkovitz et al. (2020) and has gained considerable interest. Prior work has focused on minimizing the price of explainability for specific cluster…
graph partitioningThe Marked Edge Walk: A Novel MCMC Algorithm for Sampling of Graph Partitions
Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans through graph partitioning. However, existing algorithms such as Reversible Recombination (RevReCom) and…
graph partitioningStructure-Aware Spectral Sparsification via Uniform Edge Sampling
Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampl…
graph partitioningSurrogate Graph Partitioning for Spatial Prediction
Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remai…
Computational Efficiencygraph partitioningDecision MakingQuantum Annealing for Minimum Bisection Problem: A Machine Learning-based Approach for Penalty Parameter Tuning
The Minimum Bisection Problem is a well-known NP-hard problem in combinatorial optimization, with practical applications in areas such as parallel computing, network design, and machine learning. In this paper, we examin…
graph partitioningInfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
Graph partitioning (GP), a.k.a. community detection, is a classic problem that divides nodes of a graph into densely-connected blocks. From a perspective of graph signal processing, we find that graph Laplacian with a ne…
Community Detectiongraph partitioning