自适应k均值matlab代码-dynamic-plex-propagation:动态丛传播

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一起 k 均值 matlab 代码动态丛传播 该存储库包含动态丛传播算法的 Matlab 实现。 该算法最初是在 (Viles 2013) 中开发的,它提供了一种机制,用于在不确定的情况下随着时间的推移识别和跟踪功能网络中的社区。 这与导致癫痫发作的大脑连接的处理(例如,由 ECoG 测量)具有特定的相关性。 受 (Palla 2005) 中的 clique percolation 方法的启发,该算法在函数网络的每个时间步识别 k-plex(使用 k-plex 使算法对噪声具有鲁棒性)并将它们在每个时间步内和跨时间步骤。 因此,这允许随着时间的推移和在不确定性下跟踪功能社区的诞生和消亡。 这个 Matlab 实现是算法的基本实现,以及一些相关的工具: 模拟动态网络 汇总统计信息 可视化动态网络 这仍然是一项正在进行的工作。 本文档和存储库将使用有关如何使用算法的文档和示例进行更新。 运行算法 为了运行算法,你必须有一个无向二元图的动态邻接矩阵。 给定n个顶点和t时间步长,动态邻接矩阵C应该是n × n × t 。 矩阵中的每个值表示在特定时间两个顶点之间是否存在边。 例如C(a, b,

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