Structural diversity of a user in a social network is the number of social contexts in his/her contact neighborhood. 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., t-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. To further speed up the computation, we design a novel parallel algorithm for efficient top-k search over large graphs. The parallel algorithm computes diversity scores for a batch of vertices simultaneously using multi-threads. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

社交网络中用户的结构多样性是他/她的联系社区中社交环境的数量。结构多样性搜索的问题是要在图中找到结构多样性最大的前k个顶点。但是，当识别不同的社会环境时，现有的结构多样性模型（例如，t大小的分量，t核心和t括号）对t的输入参数敏感。为了解决这个缺点，我们提出了一种无参数的结构多样性模型。具体来说，我们建议了一种新的符号d我小号Ç ř我中号我Ñ一吨我v e c o r e，它无需参数t即可自动为各种社会环境建模。凭借d我小号Ç ř我中号我Ñ一吨我v Ê Ç ö ř Ë小号和ħ -index，对于一个顶点结构多样性得分被计算。本文研究了无参数结构多样性搜索问题。一个高效的顶ķ提出了一种设计合理的修剪上限搜索算法。为了进一步加快计算速度，我们设计了一种新颖的并行算法，可对大型图形进行有效的top- k搜索。并行算法使用多线程同时计算一批顶点的多样性分数。大量的实验结果证明了现有基于t -core模型的参数敏感性，并证明了我们方法的优越性。