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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
A Dichotomy Hierarchy Characterizing Linear Time Subgraph...
Daniel Paul-Pena, C. Seshadhri · 2023-11-16 · via cs.DS updates on arXiv.org

Subgraph and homomorphism counting are fundamental algorithmic problems. Given a constant-sized pattern graph $H$ and a large input graph $G$, we wish to count the number of $H$-homomorphisms/subgraphs in $G$. Given the massive sizes of real-world graphs and the practical importance of counting problems, we focus on when (near) linear time algorithms are possible. The seminal work of Chiba-Nishizeki (SICOMP 1985) shows that for bounded degeneracy graphs $G$, clique and $4$-cycle counting can be done linear time. Recent works (Bera et al, SODA 2021, JACM 2022) show a dichotomy theorem characterizing the patterns $H$ for which $H$-homomorphism counting is possible in linear time, for bounded degeneracy inputs $G$. At the other end, Nešetřil and Ossona de Mendez used their deep theory of "sparsity" to define bounded expansion graphs. They prove that, for all $H$, $H$-homomorphism counting can be done in linear time for bounded expansion inputs. What lies between? For a specific $H$, can we characterize input classes where $H$-homomorphism counting is possible in linear time? We discover a hierarchy of dichotomy theorems that precisely answer the above questions. We show the existence of an infinite sequence of graph classes $\mathcal{G}_0$ $\supseteq$ $\mathcal{G}_1$ $\supseteq$ ... $\supseteq$ $\mathcal{G}_\infty$ where $\mathcal{G}_0$ is the class of bounded degeneracy graphs, and $\mathcal{G}_\infty$ is the class of bounded expansion graphs. Fix any constant sized pattern graph $H$. Let $LICL(H)$ denote the length of the longest induced cycle in $H$. We prove the following. If $LICL(H) < 3(r+2)$, then $H$-homomorphisms can be counted in linear time for inputs in $\mathcal{G}_r$. If $LICL(H) \geq 3(r+2)$, then $H$-homomorphism counting on inputs from $\mathcal{G}_r$ takes $Ω(m^{1+γ})$ time. We prove similar dichotomy theorems for subgraph counting.