2022年3月27日 · 作者提出OSLOM (Order Statistics Local Optimization Method)，是第一种能够检测出包括有向、有权、重叠社区、层次结构和动态社区的网络中的社区的方法。 该方法基于适应度函数的局部优化，该适应度函数表示社区相对于随机波动的统计意义，该适应度函数使用极值和order（顺序）统计工具估计的。 是第一种基于统计 显著性 在网络中找到社区的方法。 主要问题： 现在的大多数网络不能处理好很多重要的网络特征与特殊结构，比如不容易（不能）扩展到 …

OSLOM (Order Statistics Local Optimization Method) is a dynamic method based on the local optimization of cluster statistical significance subject to random fluctuations.

The fastest version of OSLOM2 clustering algorithm with slightly extended I/O for the benchmarking under Clubmark. The original sources of OSLOM2 v2.5 are taken from …

OSLOM stands for Order Statistics Local Optimization Method and it's a clustering algorithm designed for networks. You can obtain a copy of OSLOM from: http://www.oslom.org/

2010年12月10日 · In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics.

2018年12月12日 · OSLOM stands for Order Statistics Local Optimization Method and it's a clustering algorithm designed for networks. You can obtain a copy of OSLOM from: http://www.oslom.org/

OSLOM means Order Statistics Local Optimization Method and it's a clustering algorithm designed for networks. Download the code (beta version 2.4, last update: September, 2011)

In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics.

2024年7月29日 · The OSLOM algorithm starts by identifying cliques in the network and assigning them to their own communities. It then iteratively adds nodes to the existing communities or creates new communities based on a local optimization procedure.

In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics.