Finding groups in networks is very common in many practical applications, and most work mainly focus on dense groups. However, in scenarios like reviewer selection or weak social friends recommendation, we need to emphasize the privacy of individuals or minimize the possibility of information dissemination. So the internal relationship between individuals should be as tenuous as possible, but existing works cannot suit well to the requirement. Some works have focused on finding tenuous groups. However, these works only aim to find the most tenuous group and do not consider containing certain vertices. In this paper, we study the problem of finding tenuous groups in attributed networks that contain specific vertices. We first propose a new problem called Tenuous Attributed Group Query, and a new indicator, k-tenuity, to measure the structural tenuity of a group. Then we propose a method TAG-Basic to find proper groups by gradually selecting the vertices with optimal influence. We further design an advanced method TAG-ADV to improve the efficiency by forming a candidate set before selecting the optimal vertex. Experiment results show that k-tenuity is more effective than other state-of-the-art measurements, and our methods obtain the best result on group quality compared with other benchmark methods.

中文翻译：

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在属性网络中查询脆弱组

在许多实际应用中，在网络中查找组非常普遍，并且大多数工作主要集中在密集组上。但是，在审稿人选择或社交朋友推荐不力的情况下，我们需要强调个人的隐私或将信息传播的可能性降至最低。因此，人与人之间的内部关系应尽可能地细微，但现有作品不能很好地满足要求。一些作品着重于寻找脆弱的群体。但是，这些作品仅旨在找到最脆弱的组，而不考虑包含某些顶点。在本文中，我们研究了在包含特定顶点的属性网络中查找连续群的问题。我们首先提出一个新问题，即“脆弱属性组查询”和一个新指标k-tenuity，衡量组的结构强度。然后，我们提出了一种TAG-Basic方法，通过逐渐选择具有最佳影响力的顶点来找到合适的组。我们进一步设计了一种先进的方法TAG-ADV，通过在选择最佳顶点之前形成候选集来提高效率。实验结果表明，k强度比其他现有技术更有效，并且与其他基准方法相比，我们的方法在组质量上获得了最佳结果。