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Separation axiom $S_3$ for geodesic convexity in graphs

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arxiv 2405.07512 v1 pith:IOC42IPQ submitted 2024-05-13 math.CO cs.DMmath.MG

classification math.COcs.DMmath.MG
keywords graphsconvexsemispacessetsconvexitymaximalseparationcliques
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abstract

Semispaces of a convexity space $(X,C)$ are maximal convex sets missing a point. The separation axiom $S_3$ asserts that any point $x_0\in X$ and any convex set $A$ not containing $x_0$ can be separated by complementary halfspaces (convex sets with convex complements) or, equivalently, that all semispaces are halfspaces. In this paper, we study $S_3$ for geodesic convexity in graphs and the structure of semispaces in $S_3$-graphs. We characterize $S_3$-graphs and their semispaces in terms of separation by halfspaces of vertices $x_0$ and special sets, called maximal $x_0$-proximal sets and in terms of convexity of their mutual shadows $x_0/K$ and $K/x_0$. In $S_3$-graphs $G$ satisfying the triangle condition (TC), maximal proximal sets are the pre-maximal cliques of $G$ (i.e., cliques $K$ such that $K\cup\{ x_0\}$ are maximal cliques). This allows to characterize the $S_3$-graphs satisfying (TC) in a structural way and to enumerate their semispaces efficiently. In case of meshed graphs (an important subclass of graphs satisfying (TC)), the $S_3$-graphs have been characterized by excluding five forbidden subgraphs. On the way of proving this result, we also establish some properties of meshed graphs, which maybe of independent interest. In particular, we show that any connected, locally-convex set of a meshed graph is convex. We also provide several examples of $S_3$-graphs, including the basis graphs of matroids. Finally, we consider the (NP-complete) halfspace separation problem, describe two methods of its solution, and apply them to particular classes of graphs and graph-convexities.

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  1. Efficient Algorithms for Learning and Compressing Monophonic Halfspaces in Graphs

    cs.LG 2025-06 conditional novelty 7.0 of 10

    Monophonic halfspaces in graphs admit a 2-SAT-based cell decomposition that yields polynomial-time ERM and near-optimal PAC, active, online, and teaching algorithms.

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