基于随机游走的Louvain算法改进
An improvement on the Louvain algorithm using random walks
AI总结：
本文改进Newman谱方法和Louvain社区检测算法，用随机游走替代特征值计算，并作为额外阶段细化簇，在保持复杂度同时提升效率，实验验证了鲁棒性。
摘要：
我们将展示对著名社区检测算法的改进，即Newman谱方法算法和Louvain算法。Newman算法首先将原始图视为单个簇，然后重复将每个簇一分为二的过程，依据第二大特征值对应的特征向量的符号进行划分。我们的改进在于在分裂过程中用随机游走替代耗时的特征值计算。Louvain算法迭代执行以下步骤，直到模块度不再增加：每一步包含两个阶段，阶段1将图划分为簇，阶段2构建一个新图，其中每个顶点代表阶段1得到的一个簇。我们提出对该算法的改进，通过添加我们的随机游走算法作为额外阶段来细化阶段1获得的簇。它保持了与Louvain算法相当的复杂度，同时展现出更高的效率。为验证所提算法的鲁棒性和有效性，我们使用随机生成图和真实世界数据进行了实验。
英文摘要：
We will present improvements to famous algorithms for community detection, namely Newman's spectral method algorithm and the Louvain algorithm. The Newman algorithm begins by treating the original graph as a single cluster, then repeats the process to split each cluster into two, based on the signs of the eigenvector corresponding to the secondlargest eigenvalue. Our improvement involves replacing the time-consuming computation of eigenvalues with a random walk during the splitting process. The Louvain algorithm iteratively performs the following steps until no increase in modularity can be achieved anymore: each step consists of two phases, phase 1 for partitioning the graph into clusters, and phase 2 for constructing a new graph where each vertex represents one cluster obtained from phase 1. We propose an improvement to this algorithm by adding our random walk algorithm as an additional phase for refining clusters obtained from phase 1. It maintains a complexity comparable to the Louvain algorithm while exhibiting superior efficiency. To validate the robustness and effectiveness of our proposed algorithms, we conducted experiments using randomly generated graphs and real-world data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
