论文标题
有效的算法基于非折叠式矩阵，用于签名网络中的社区检测
Efficient algorithm based on non-backtracking matrix for community detection in signed networks
论文作者
论文摘要
社区检测或聚类是理解复杂系统结构的关键任务。在某些网络中，允许节点通过“正”或“负”边缘链接；此类网络称为签名网络。在签名网络中发现社区比未签名网络中的社区更具挑战性。在这项研究中，我们创新地开发了签名网络的非折线矩阵，理论上从理论上得出了该矩阵的可检测性阈值，并证明了使用矩阵进行社区检测的可行性。我们通过考虑网络中的平衡路径（称为平衡的非背带矩阵）进一步改善了开发的矩阵。仿真结果表明，基于平衡的非背带矩阵的算法显着胜过基于邻接矩阵和符号非折叠式矩阵的算法。提出的（改进的）矩阵显示出检测有或没有重叠的社区的巨大潜力。
Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either "positive" or "negative" edges; such networks are called signed networks. Discovering communities in signed networks is more challenging than that in unsigned networks. In this study, we innovatively develop a non-backtracking matrix of signed networks, theoretically derive a detectability threshold for this matrix, and demonstrate the feasibility of using the matrix for community detection. We further improve the developed matrix by considering the balanced paths in the network (referred to as a balanced non-backtracking matrix). Simulation results demonstrate that the algorithm based on the balanced nonbacktracking matrix significantly outperforms those based on the adjacency matrix and the signed non-backtracking matrix. The proposed (improved) matrix shows great potential for detecting communities with or without overlap.
