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Cliques in High-Dimensional Random Geometric Graphs

机译:高维随机几何图中的派系

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Random geometric graphs are good examples of random graphs with a tendency to demonstrate community structure. Vertices of such a graph are represented by points in Euclid space R~d, and edge appearance depends on the distance between the points. Random geometric graphs were extensively explored and many of their basic properties are revealed. However, in the case of growing dimension d → ∞ practically nothing is known; this regime corresponds to the case of data with many features, a case commonly appearing in practice. In this paper, we focus on the cliques of these graphs in the situation when average vertex degree grows significantly slower than the number of vertices n with n → ∞ and d → ∞. We show that under these conditions random geometric graphs do not contain cliques of size 4 a.s. As for the size 3, we will present new bounds on the expected number of triangles in the case log~2 n ? d ? log~3 n that improve previously known results.
机译:随机几何图是随机图的良好示例,其具有展示群落结构的趋势。这种曲线图的顶点由欧几里德空间R〜D中的点表示,边缘外观取决于点之间的距离。随机探索随机几何图,并揭示了许多基本属性。然而,在生长尺寸D→∞实际上没有任何内容的情况下;该制度对应于具有许多特征的数据的情况,其常见于实践中出现的情况。在本文中,我们专注于这些图中的这些图表中的群体,当平均顶点度生长显着慢的顶点n与n→∞和d→∞的顶点n显着较慢。我们表明,在这些条件下,随机几何图不包含大小4 A.的批号。至于尺寸3,我们将在案例日志〜2 n的预期三角数上呈现新的界限? d?日志〜3 n,可改善先前已知的结果。

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