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louvain算法的论文中提到,将节点移动到另一个社区时需要考虑两个步骤,将节点从原社区移出时的模块度变化和将节点移入新社区时的模块度变化,这个项目中的实现似乎只考虑了后者?
模块度的推导过程中涉及到Aij的求和,这意味着每条边(除了自环边)会被考虑两次(邻接矩阵中[i,j]和[j,i]),也就意味着k_in(节点与社区的边权和)应该乘以2,这个项目似乎没有考虑到这点。
压缩了图后,每个节点应该是有自环边,这个自环边我觉得也应该被考虑到k_in中,而且仅计算一次(邻接矩阵中[i,i])。而如果考虑了1中的两个步骤,自环边对k_in的影响就会相互抵消,无需考虑了。
可参考的公式推导: https://web.stanford.edu/class/cs246/slides/11-graphs1.pdf
https://www.cnblogs.com/fengfenggirl/p/louvain.html
https://github.com/taynaud/python-louvain/blob/def91793772c3e77ab4167d175903a5365c24b4b/community/community_louvain.py#L470
相似的项目:https://github.com/zzz24512653/CommunityDetection
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louvain算法的论文中提到,将节点移动到另一个社区时需要考虑两个步骤,将节点从原社区移出时的模块度变化和将节点移入新社区时的模块度变化,这个项目中的实现似乎只考虑了后者?
模块度的推导过程中涉及到Aij的求和,这意味着每条边(除了自环边)会被考虑两次(邻接矩阵中[i,j]和[j,i]),也就意味着k_in(节点与社区的边权和)应该乘以2,这个项目似乎没有考虑到这点。
压缩了图后,每个节点应该是有自环边,这个自环边我觉得也应该被考虑到k_in中,而且仅计算一次(邻接矩阵中[i,i])。而如果考虑了1中的两个步骤,自环边对k_in的影响就会相互抵消,无需考虑了。
可参考的公式推导:
https://web.stanford.edu/class/cs246/slides/11-graphs1.pdf
https://www.cnblogs.com/fengfenggirl/p/louvain.html
https://github.com/taynaud/python-louvain/blob/def91793772c3e77ab4167d175903a5365c24b4b/community/community_louvain.py#L470
相似的项目:https://github.com/zzz24512653/CommunityDetection
The text was updated successfully, but these errors were encountered: