Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 215 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313729
Title: Network Clustering with Entropy-Based Monte Carlo Method
Author(s): Qiannan Zhai* and Fangyuan Zhang
Companies: Texas Tech University and Texas Tech University
Keywords: Clustering; Network; Cross-Entropy; Monte Carlo

A network clustering algorithm is useful for understanding the structure of network data. Clustering methods usually group vertices based on a certain similarity/distance measure, such that vertices assigned in the same clusters are more closely related than vertices in different clusters. Therefore, network clustering can be formulated as an optimal problem to maximize the contrast of within-cluster and between-cluster closeness. However, most of the existing clustering methods are greedy algorithm. In this poster, we explore the usage of a cross-entropy Monte Carlo method for solving such a combinatorial problem. Instead of placing a discrete uniform distribution on all the potential solutions, an iterative importance sampling technique is utilized “to slowly tighten the net” to place most distributional mass on the optimal community structure and its neighbors. Simulation studies were conducted to assess the performance of the method.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program