论文标题
网络上的线性聚类过程
Linear Clustering Process on Networks
论文作者
论文摘要
我们在由两个相反力的网络上提出了一个线性聚类过程：相邻节点之间的吸引力和排斥。每个节点都映射到一维线上的位置。吸引力和排斥力移动线上的淋巴结位置，具体取决于两个相邻节点的邻域的相似性或不同。基于每个节点位置，估计网络中的簇数，以及每个节点的群集成员资格。提出的线性群集过程的性能是在合成网络上基准的，可针对被广泛接受的聚类算法（例如模块化，Louvain方法和非背包跟踪矩阵）进行基准测试。所提出的线性聚类过程优于最流行的基于模块化的方法，例如Louvain方法，同时具有可比的计算复杂性。
We propose a linear clustering process on a network consisting of two opposite forces: attraction and repulsion between adjacent nodes. Each node is mapped to a position on a one-dimensional line. The attraction and repulsion forces move the nodal position on the line, depending on how similar or different the neighbourhoods of two adjacent nodes are. Based on each node position, the number of clusters in a network, together with each node's cluster membership, is estimated. The performance of the proposed linear clustering process is benchmarked on synthetic networks against widely accepted clustering algorithms such as modularity, the Louvain method and the non-back tracking matrix. The proposed linear clustering process outperforms the most popular modularity-based methods, such as the Louvain method, while possessing a comparable computational complexity.
