用于社会网络分析的批处理核SOM及相关拉普拉斯方法
Batch kernel SOM and related Laplacian methods for social network analysis
摘要：
大型图是自然的数学模型，用于描述数据的结构，在各种各样的领域，如Web挖掘，社交网络，信息检索，生物网络等，所有这些应用程序，自动工具需要得到一个综合视图的图形，并达到一个很好的理解的根本问题。特别是，发现紧密连接的顶点群并理解这些群之间的关系在实践中是非常重要的。本文展示了如何内核版本的批处理自组织映射可以用来实现这些目标，通过内核来自拉普拉斯矩阵的图形，特别是当它与更经典的方法结合使用时，基于频谱分析的图形。所提出的方法是用来探索中世纪的社会网络建模，通过加权图，已直接从一个大型语料库的农业合同的结构。
英文摘要：
Large graphs are natural mathematical models for describing the structure of the data in a wide variety of fields, such as web mining, social networks, information retrieval, biological networks, etc. For all these applications, automatic tools are required to get a synthetic view of the graph and to reach a good understanding of the underlying problem. In particular, discovering groups of tightly connected vertices and understanding the relations between those groups is very important in practice. This paper shows how a kernel version of the batch Self Organizing Map can be used to achieve these goals via kernels derived from the Laplacian matrix of the graph, especially when it is used in conjunction with more classical methods based on the spectral analysis of the graph. The proposed method is used to explore the structure of a medieval social network modeled through a weighted graph that has been directly built from a large corpus of agrarian contracts.
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。
