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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
Graph4J -- A computationally efficient Java library for g...
Cristian Frăsinaru, Emanuel Florentin Olariu · 2023-08-19 · via cs.DS updates on arXiv.org

Graph algorithms play an important role in many computer science areas. In order to solve problems that can be modeled using graphs, it is necessary to use a data structure that can represent those graphs in an efficient manner. On top of this, an infrastructure should be build that will assist in implementing common algorithms or developing specialized ones. Here, a new Java library is introduced, called Graph4J, that uses a different approach when compared to existing, well-known Java libraries such as JGraphT, JUNG and Guava Graph. Instead of using object-oriented data structures for graph representation, a lower-level model based on arrays of primitive values is utilized, that drastically reduces the required memory and the running times of the algorithm implementations. The design of the library, the space complexity of the graph structures and the time complexity of the most common graph operations are presented in detail, along with an experimental study that evaluates its performance, when compared to the other libraries. Emphasis is given to infrastructure related aspects, that is graph creation, inspection, alteration and traversal. The improvements obtained for other implemented algorithms are also analyzed and it is shown that the proposed library significantly outperforms the existing ones.