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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
Terminating cases of flooding
Walter Hussak, Amitabh Trehan · 2020-09-12 · via cs.DS updates on arXiv.org

Basic synchronous flooding proceeds in rounds. Given a finite undirected (network) graph $G$, a set of sources $I \subseteq G$ initiate flooding in the first round by every node in $I$ sending the same message to all of its neighbours. In each subsequent round, nodes send the message to all of their neighbours from which they did not receive the message in the previous round. Flooding terminates when no node in $G$ sends a message in a round. The question of termination has not been settled - rather, non-termination is implicitly assumed to be possible. We show that flooding terminates on every finite graph. In the case of a single source $g_0$, flooding terminates in $e$ rounds if $G$ is bipartite and $j$ rounds with $e < j \leq e+d+1$ otherwise, where $e$ and $d$ are the eccentricity of $g_0$ and diameter of $G$ respectively. For communication/broadcast to all nodes, this is asymptotically time optimal and obviates the need for construction and maintenance of spanning structures. We extend to dynamic flooding initiated in multiple rounds with possibly multiple messages. The cases where a node only sends a message to neighbours from which it did not receive {\it any} message in the previous round, and where a node sends some highest ranked message to all neighbours from which it did not receive {\it that} message in the previous round, both terminate. All these cases also hold if the network graph loses edges over time. Non-terminating cases include asynchronous flooding, flooding where messages have fixed delays at edges, cases of multiple-message flooding and cases where the network graph acquires edges over time.