Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings.

通常在社区中发现连接良好的用户，这是网络数据分析最重要的任务之一，具有巨大的实际应用。近年来，属性图中的社区搜索开始受到关注，其目的是寻找结构和属性内聚的社区。同时，搜索一个结构有凝聚力但属性多样化的社区，称为属性多元化社区搜索，还处于初级阶段。在本文中，我们介绍了我们最近为发现属性多元化社区所做的努力。事实上，对于不同的应用，对社区建模的属性多样化的需求是有很大差异的。我们介绍了三种属性多元化社区模型，其中属性多元化扮演不同的角色，作为目标和约束。我们还讨论了加速属性多样化社区搜索的主要技术。我们进行了广泛的实验，以展示我们的算法在各种环境中寻找属性多样化社区的有效性和效率。