惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

云风的 BLOG
云风的 BLOG
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
月光博客
月光博客
人人都是产品经理
人人都是产品经理
宝玉的分享
宝玉的分享
博客园 - 司徒正美
WordPress大学
WordPress大学
Microsoft Azure Blog
Microsoft Azure Blog
罗磊的独立博客
Vercel News
Vercel News
T
The Blog of Author Tim Ferriss
T
Tailwind CSS Blog
A
About on SuperTechFans
Apple Machine Learning Research
Apple Machine Learning Research
L
LangChain Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
V
Visual Studio Blog
S
SegmentFault 最新的问题
Google DeepMind News
Google DeepMind News
博客园 - 聂微东

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Weighted degrees and truncated derived bibliographic netw...
Vladimir Batagelj · 2024-01-10 · via cs.DS updates on arXiv.org

Large bibliographic networks are sparse -- the average node degree is small. This is not necessarily true for their product -- in some cases, it can ``explode'' (it is not sparse, increases in time and space complexity). An approach in such cases is to reduce the complexity of the problem by limiting our attention to a selected subset of important nodes and computing with corresponding truncated networks. The nodes can be selected by different criteria. An option is to consider the most important nodes in the derived network -- nodes with the largest weighted degree. It turns out that the weighted degrees in the derived network can be computed efficiently without computing the derived network itself.