基于Infomap聚类的多层次图形绘制
Multi-level Graph Drawing using Infomap Clustering
摘要：
信息地图聚类发现社区结构，使随机游走轨迹的预期描述长度最小化;信息地图聚类的算法在大型图的实践中运行得很快。在本文中，我们利用的有效性信息地图聚类结合多级图形绘制范例。实验表明，我们的新的基于信息地图的多级算法产生良好的大型和复杂的网络的可视化，与质量指标的显着改善。
英文摘要：
Infomap clustering finds the community structures that minimize the expected description length of a random walk trajectory; algorithms for infomap clustering run fast in practice for large graphs. In this paper we leverage the effectiveness of Infomap clustering combined with the multi-level graph drawing paradigm. Experiments show that our new Infomap based multi-level algorithm produces good visualization of large and complex networks, with significant improvement in quality metrics.
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。
