| 标题 |
[求助补充材料] [高分]
 专利、报告等 Fairness-aware Maximal Clique in Large Graphs: Concepts and Algorithms 大图中的公平性感知极大团：概念与算法
相关领域
集团
枚举
计算机科学
算法
修剪
诱导子图
诱导子图同构问题
子图同构问题
组合数学
数学
离散数学
理论计算机科学
图形
折线图
顶点（图论）
生物
电压图
农学
|
| 备注 |
只需要case study部分的数据集
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| 网址 | |
| DOI | |
| 其它 | We conduct a case study on a collaboration network DBLP to evaluate the effectiveness of our algorithms. The DBLP dataset is downloaded from dblp.uni-trier.de/xml/. We extract a subgraph DBCS from DBLP which contains the authors who had published at least one paper in the database (DB), data mining (DM ), and artificial intelligence (AI) related conferences. The DBCS subgraph contains 52,106 vertices (authors) and 341,382 undirected edges. The attribute A represents the author's main research area with Aval = {DB, DM, AI}. Each vertex has one attribute value selected from the set Aval. We set the attribute value for each vertex based on the maximum number of papers that the author published in the related conferences. For example, if an author has published 20 papers in DB related conferences and 5 papers in DM related conferences, we choose DB as the author's attribute value. |
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