论文标题
超图中的随机散步和社区检测
Random walks and community detection in hypergraphs
论文作者
论文摘要
我们建议在超图上进行一个随机步行过程的一个参数家族，其中参数偏向沃克的动力学降低了低或高基数的高音。我们表明，对于参数的每个值，结果过程定义了其在加权网络上的超图投影。然后，我们通过考虑与每个随机步行过程相关的社区结构来探索它们之间的差异。为此，我们将马尔可夫稳定性框架推广到超图并在人工和现实世界中的超图上进行测试。
We propose a one parameter family of random walk processes on hypergraphs, where a parameter biases the dynamics of the walker towards hyperedges of low or high cardinality. We show that for each value of the parameter the resulting process defines its own hypergraph projection on a weighted network. We then explore the differences between them by considering the community structure associated to each random walk process. To do so, we generalise the Markov stability framework to hypergraphs and test it on artificial and real-world hypergraphs.
