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Graph Sparsification by Effective Resistances
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A new approach to the maximum-flow problem
What points at this work
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Decentralized computation of effective resistances and acceleration of consensus algorithms
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pdGRASS: A Fast Parallel Density-Aware Algorithm for Graph Spectral Sparsification
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Explicitly Defined Sampling Categories for ESA on a Bipartite Graph
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Linear Quantization by Effective-Resistance Sampling
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Weighted Gradient Coding with Leverage Score Sampling
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Edge Sampling of Graphs Based on Edge Smoothness
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pGRASS-Solver: A Parallel Iterative Solver for Scalable Power Grid Analysis Based on Graph Spectral Sparsification
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Accuracy-Preserving Reduction of Sparsified Reduced Power Grids with A Multilevel Node Aggregation Scheme
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Hardware-Aware Spectral Sparsification for Efficient GNN Training under Memory Constraints
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Graph Neural Network Architectures for Complex Pattern Modelling
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Fast and Accurate Graph Stream Summarization
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Edge Sparsification for Graphs via Meta-Learning
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Opinion Dynamics Incorporating Higher-Order Interactions
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A Generic Graph Sparsification Framework using Deep Reinforcement Learning
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Towards Scalable Spectral Sparsification of Directed Graphs
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Leveraging k-Weighted Dominating Set for Graph Sparsification
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Design of Sampling Matrices in Graph Frequency Domain for Graph Signal Processing
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Spectral sparsification in spectral clustering
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Semi-supervised Graph Rewiring with the Dirichlet Principle
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New Graph Distance Measures and Matching of Topological Maps for Robotic Exploration
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Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
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Spectral partitioning for node criticality
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Distributed Local Sketching for £2 Embeddings
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Analysis of very large resistive networks using low distortion embedding
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Constructing small-signal equivalent impedances using ellipsoidal norms
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Toeplitz-Plus-Hankel Matrix Recovery for Green’s Function Computations on General Substrates
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Topology-Based Spectral Sparsification
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Estimation With Fast Feature Selection in Robot Visual Navigation
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Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects
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Sublinear-Time Attraction Force Computation for Large Complex Graph Drawing
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Sublinear-time Algorithms for Stress Minimization in Graph Drawing
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