当前位置: X-MOL 学术Phys. Lett. A › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Community detection based on first passage probabilities
Physics Letters A ( IF 2.3 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.physleta.2020.127099
Zhaole Wu , Xin Wang , Wenyi Fang , Longzhao Liu , Shaoting Tang , Hongwei Zheng , Zhiming Zheng

Community detection is of fundamental significance for understanding the topology characters and the spreading dynamics on complex networks. While random walk is widely used and is proven effective in many community detection algorithms, there still exists two major defects: (i) the maximal length of random walk is too large to distinguish the clustering information if using the average step of all possible random walks; (ii) the useful community information at all other step lengths are missed if using a pre-assigned maximal length. In this paper, we propose a novel community detection method based on the first passage probabilities (FPPM), equipped with a new similarity measure that incorporates the complete structural information within the maximal step length. Here the diameter of the network is chosen as an appropriate boundary of random walks which is adaptive to different networks. Then we use the hierarchical clustering to group the vertices into communities and further select the best division through the corresponding modularity values. Finally, a post-processing strategy is designed to integrate the unreasonable small communities, which significantly improves the accuracy of community division. Surprisingly, the numerical simulations show that FPPM performs best compared to several classic algorithms on both synthetic benchmarks and real-world networks, which reveals the universality and effectiveness of our method.

中文翻译:

基于首次通过概率的社区检测

社区检测对于理解复杂网络的拓扑特征和传播动态具有重要意义。虽然随机游走被广泛使用并在许多社区检测算法中被证明是有效的,但仍然存在两个主要缺陷:(i)如果使用所有可能的随机游走的平均步长,随机游走的最大长度太大而无法区分聚类信息; (ii) 如果使用预先分配的最大长度,则会错过所有其他步长的有用社区信息。在本文中,我们提出了一种基于首次通过概率(FPPM)的新社区检测方法,该方法配备了一种新的相似性度量,该方法在最大步长内结合了完整的结构信息。这里选择网络的直径作为随机游走的适当边界,它可以适应不同的网络。然后我们使用层次聚类将顶点分组为社区,并通过相应的模块化值进一步选择最佳划分。最后,设计了一种后处理策略来整合不合理的小社区,显着提高了社区划分的准确性。令人惊讶的是,数值模拟表明,与几种经典算法相比,FPPM 在合成基准和现实世界网络上的表现最好,这揭示了我们方法的普遍性和有效性。然后我们使用层次聚类将顶点分组为社区,并通过相应的模块化值进一步选择最佳划分。最后,设计了一种后处理策略来整合不合理的小社区,显着提高了社区划分的准确性。令人惊讶的是,数值模拟表明,与几种经典算法相比,FPPM 在合成基准和现实世界网络上的表现最好,这揭示了我们方法的普遍性和有效性。然后我们使用层次聚类将顶点分组为社区,并通过相应的模块化值进一步选择最佳划分。最后,设计了一种后处理策略来整合不合理的小社区,显着提高了社区划分的准确性。令人惊讶的是,数值模拟表明,与几种经典算法相比,FPPM 在合成基准和现实世界网络上的表现最好,这揭示了我们方法的普遍性和有效性。
更新日期:2021-02-01
down
wechat
bug