Markov clustering is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space. However, its community detection performance in complex networks has been demonstrating results far from the state of the art methods such as Infomap and Louvain. The crucial issue is to convert the unweighted network topology in a ‘smart-enough’ pre-weighted connectivity that adequately steers the stochastic flow procedure behind Markov clustering. Here we introduce a conceptual innovation and we discuss how to leverage network latent geometry notions in order to design similarity measures for pre-weighting the adjacency matrix used in Markov clustering community detection. Our results demonstrate that the proposed strategy improves Markov clustering significantly, to the extent that it is often close to the performance of current state of the art methods for community detection. These findings emerge considering both synthetic ‘realistic’ networks (with known ground-truth communities) and real networks (with community metadata), and even when the real network connectivity is corrupted by noise artificially induced by missing or spurious links. Our study enhances the generalized understanding of how network geometry plays a fundamental role in the design of algorithms based on network navigability.

马尔可夫聚类是一种有效的无监督模式识别算法，用于在高维特征空间中进行数据聚类。但是，其在复杂网络中的社区检测性能已证明其结果远非诸如Infomap和Louvain之类的最新方法。关键问题是将未加权的网络拓扑转换为“足够智能”的预加权连接，​​以充分控制Markov群集背后的随机流过程。在这里，我们介绍了一种概念上的创新，并讨论了如何利用网络潜在的几何概念来设计相似性度量，以对Markov聚类社区检测中使用的邻接矩阵进行预加权。我们的结果表明，所提出的策略可显着改善Markov聚类，在某种程度上讲，它通常接近于最新的社区检测方法的性能。考虑到合成的“现实”网络（具有已知的地面真人社区）和真实网络（具有社区元数据），甚至在真实网络连接由于丢失或虚假链接而人为引起的噪声破坏的情况下，这些发现也出现了。我们的研究增强了对网络几何如何在基于网络可导航性的算法设计中起基本作用的普遍理解。甚至在实际网络连接因丢失或虚假链接而人为引起的噪声破坏的情况下。我们的研究增强了对网络几何如何在基于网络可导航性的算法设计中起基本作用的普遍理解。甚至在实际网络连接因丢失或虚假链接而人为引起的噪声破坏的情况下。我们的研究增强了对网络几何如何在基于网络可导航性的算法设计中起基本作用的普遍理解。