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Parameter-Free Structural Diversity Search

机译:无参数结构多样性搜索

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摘要

The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., f-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.
机译:结构多样性搜索问题是在图形中找到具有最大结构分集的顶级顶点。然而,当识别不同的社交环境时,现有的结构分集模型(例如,F大小的组件,T-Core和T-Brace)对T的输入参数敏感。为了解决此缺点,我们提出了一种无参数结构分集模型。具体而言,我们提出了一种歧视性核心的新颖符号,其自动模拟各种社会环境而没有参数t。利用鉴别性核心和H折射率,计算顶点的结构分集分数。我们研究了本文的无参数结构多样性搜索问题。提出了一种有效的Top-K搜索算法,具有设计精心设计的修剪。广泛的实验结果证明了现有的基于T核的模型的参数灵敏度,并验证了我们方法的优越性。

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