The detection of community structure is probably one of the hottest trends in complex network research as it reveals the internal organization of people, molecules or processes behind social, biological or computer networks\dots The issue is to provide a network partition representative of this organization so that each community presumably gathers nodes sharing a common mission, purpose or property. Usually the identification is based on the difference between the connectivity density of the interior and the boundary of a community. Indeed, nodes sharing a common purpose or property are expected to interact closely. Although this rule appears mostly relevant, some fundamental scientific problems like disease module detection highlight the inability to determine significantly the communities under this connectivity rule. The main reason is that the connectivity density is not correlated to a shared property or purpose. Therefore, another paradigm is required for properly formalize this issue in order to meaningfully detect these communities. In this article we study the community formation from this new principle. Considering colors formally figures the shared properties, the issue is thus to maximize group of nodes with the same color within communities.. We study this novel community framework by introducing new measurement called \emph{chromarity} assessing the quality of the community structure regarding this constraint. Next we propose an algorithm solving the community structure detection based on this new community formation paradigm.

翻译：社区结构检测可能是复杂网络研究中最热门的方向之一，因为它揭示了社会、生物或计算机网络背后的人群、分子或过程的内部组织方式。该问题旨在提供一种能代表这种组织的网络划分，使得每个社区内的节点共享共同的任务、目的或属性。通常，这种识别基于社区内部与边界连接密度的差异：共享共同目的或属性的节点理应具有紧密的交互关系。尽管该规则在多数情况下具有相关性，但疾病模块检测等基础科学问题揭示了在这种连接规则下难以有效确定社区。其主要原因在于连接密度与共享属性或目的之间并不存在关联。因此，需要另一种范式来恰当地形式化该问题，以有意义地检测这些社区。本文从这一新原理出发研究社区形成机制。考虑到颜色可形式化地表示共享属性，该问题转化为最大化社区内具有相同颜色的节点群组。我们通过引入名为"色度（chromarity）"的新指标来研究这一新型社区框架，该指标用于评估在此约束下社区结构的质量。随后，我们提出一种基于这种新型社区形成范式的算法，用于解决社区结构检测问题。