Many real-world networks own the characteristic of anti-community structure, i.e. disassortative structure, where nodes share no or few connections inside their groups but most of their connections outside. Detecting anti-community structure can explore negative relations among objects. However, the structures output by the existing algorithms are unbalanced, leading to no or few negative relations to be explored in some groups. Stochastic block models are promising methods for exploring disassortative structures in networks, but their results are highly dependent on the observed structure of a network. In this paper, we first improve the classic stochastic block model and propose a No sElf-edge Stochastic blOck Model (NESOM) for anti-community structure. NESOM considers the edges inside and among groups, respectively, and evolves a new objective function for evaluating anti-community structure. And then, a new heuristic algorithm NESOM-AC is proposed for balanced anti-community detection, which consists of three stages: creation of initial structure, decomposition of redundant group, and adjustment of group membership. Inspired by NESOM, we finally develop a new synthetic benchmark NESOM-Net for performance comparison. Experimental results on NESOM-Net with up to 100,000 nodes and 16 real-world networks demonstrate the effectiveness of NESOM-AC in anti-community detection when compared with five state-of-the-art algorithms.

许多现实世界的网络都具有反社区结构（即分散结构）的特征，在这种结构中，节点在组内不共享或只有很少的连接，而在组外则共享大多数的连接。检测反社区结构可以探索对象之间的负相关关系。但是，现有算法输出的结构不平衡，导致在某些组中没有或几乎没有负关系要探索。随机块模型是探索网络中分解结构的有前途的方法，但是其结果高度依赖于观察到的网络结构。在本文中，我们首先改进了经典的随机块模型，并提出了一种针对反社区结构的无安全边随机blOck模型（NESOM）。NESOM分别考虑组内部和组之间的边缘，并开发了评估反社区结构的新目标函数。然后，提出了一种新的启发式算法NESOM-AC，用于平衡的反社区检测，包括三个阶段：初始结构的创建，冗余组的分解和组成员的调整。在NESOM的启发下，我们最终开发了一种新的综合基准NESOM-Net，用于性能比较。在具有多达100,000个节点和16个真实世界网络的NESOM-Net上的实验结果证明，与五种最新算法相比，NESOM-AC在反社区检测中的有效性。和调整组成员身份。在NESOM的启发下，我们最终开发了一种新的综合基准NESOM-Net，用于性能比较。在具有多达100,000个节点和16个真实世界网络的NESOM-Net上的实验结果证明，与五种最新算法相比，NESOM-AC在反社区检测中的有效性。和调整组成员身份。在NESOM的启发下，我们最终开发了一种新的综合基准NESOM-Net，用于性能比较。在具有多达100,000个节点和16个真实世界网络的NESOM-Net上的实验结果证明，与五种最新算法相比，NESOM-AC在反社区检测中的有效性。