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Fast GN
- Fast Community Detection algorithm 是美国Michigan大学Newman教授所创，是复杂网络中社区发现的经典算法
GN-algorithm-for-matlab
- 这是复杂网络社区划分的GN算法，希望对大家有帮助.-This community is divided on the GN algorithm source code, we hope to help
经典算法GN算法的Java实现
- 社区发现经典算法GN算法的Java实现，能够根据不同节点之间的联系来实现社区的划分。-Community found that classical algorithm GN algorithm Java implementation, and can be the link between different nodes to achieve the division of the community.
GN
- 用于社区结构划分，GN算法是最受认可的社区结构划分算法-GN，social network, network community detection
GN
- 该代码是典型算法GN算法的实现，用于社区检测-The code is a typical algorithm GN algorithm for community detection
GN
- 复杂网络中社区划分算法中利用边介数的经典GN算法-Complex network community partitioning algorithm using edge betweenness classic GN algorithm
GN
- 社区发现的经典算法--GN算法的java实现-Classical algorithm community found- GN algorithm to achieve the java
GN
- GN算法是一种经典的web社区发现算法，java语言。可用实例验证-the GN algorithm is a kind of classic web community discovery algorithm
GN
- 社区发现GN算法 采用C/C++编程加以实现 可运行- Communities find GN algorithm C/C++
Desktop
- 社区发现 GN 算法完整实现，无向图和有向图，强社团等-GN devide a huge matrix
gn
- 社区发现的经典算法 GN算法的java测试-Java test of the classical algorithm GN algorithm for community detection
GN
- 该代码是典型算法GN算法的实现，用于社区检测-GN Algorithem
GN--code
- 经典社区发现算法之GN算法完整实现，亲测有效-GN algorithm of community discovery algorithm for full implementation of
GN
- GN算法是一种凝聚型的社区结构发现算法。该算法根据网络中社区内部高内聚、社区之间低内聚的特点，逐步去除社区之间的边，取得相对内聚的社区结构。GN算法是一种分裂方法[]。其基本思想是不断的从网络中移除介数最大的边。-GN algorithm is a cohesive community structure discovery algorithm. Based on the characteristics of high cohesion within the community and low
1
- GN算法是一种分裂型的社区结构发现算法。该算法根据网络中社区内部高内聚、社区之间低内聚的特点，逐步去除社区之间的边，取得相对内聚的社区结构。(GN algorithm is a divisive community structure discovery algorithm. According to the characteristics of high cohesion within the community and low cohesion between communities, th
igraph
- rigraph实现边介数社区发现算法(GN)(Rigraph implementation of edge betweenness community discovery algorithm (GN))
09498664GN_FN
- 主要用于社区GN的分类，具有较强的分类功能，代码已测试过(It is mainly used in the classification of community GN. It has a strong classification function. The code has been tested.)