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用pajek的链接分析
Link analysis with pajek |
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| 课程网址: |
http://videolectures.net/acai05_kejzar_lasn/
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| 主讲教师: |
Nataša Kejžar
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| 开课单位: |
卢布尔雅那大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
Pajek是一个用于大型网络分析和可视化的程序。它可以免费获得，供非商业使用。除了普通网络，pajek还支持多关系和时序网络。在大型网络分析中，我们经常对给定网络的重要部分感兴趣。有几种方法可以确定它们。岛屿方法是基于顶点或线的重要度量。设(V,L,p)是具有顶点性质的网络p: V ?令t为实数。如果我们删除所有顶点(和相应的链接)的属性值小于t,得到子网叫vertex-cut t水平。它的组件的数量和大小取决于t。我们常常只考虑组件的大小至少k和不超过k大小的组件小于noninteresting k被丢弃,而组件的大小大于k再次下调一些更高的水平。顶点岛是一个连通的子网络，其顶点的属性值大于其邻域内的顶点。很容易看出，顶点切割的组件都是顶点岛。我们开发了一种有效的算法，可以识别间隔k中所有最大顶点岛的大小。给定网络中的K。对于带加权线的网络，我们可以类似地定义线岛。线岛算法是基于线切割的。这两种算法都是非常通用的——它们可以应用于任何顶点/线的重要性度量。它们的复杂度是稀疏网络的次二次型——它们可以应用于非常大的网络。我们将说明它们在选定(大型)网络上应用不同的重要度量。我们还将介绍模式搜索在系谱分析中的应用，以及分析(多关系)时间网络的一些方法。
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| 课程简介: |
Pajek is a program (for Windows) for large network analysis and visualization. Besides ordinary networks Pajek supports also multi-relational and temporal networks. In large network analysis we are often interested in important parts of given network. There are several ways how to determine them. The islands approach is based on an importance measure of vertices or lines. Let (V,L,p) be a network with vertex property p : V ? R and let t be a real number. If we delete all vertices (and corresponding links) with the property value less than t, we get subnetwork called vertex-cut at level t. The number and sizes of its components depend on t. Often we consider only components of size at least k and not exceeding K. The components of size smaller than k are discarded as noninteresting, while the components of size larger than K are cut again at some higher level. Vertex-island is a connected subnetwork which vertices have greater property value than the vertices in its neighborhood. It is easy to see that the components of vertex-cuts are all vertex-islands. We developed an efficient algorithm that identifies all maximal vertex-islands of sizes in the interval k..K in a given network. For networks with weighted lines we can similarly define line-islands. The line-islands algorithm is based on line-cuts. Both algorithms are very general - they can be applied for any vertex/line importance measure. Their complexity is for sparse networks subquadratic - they can be applied to very large networks. We will illustrate them applying different importance measures on selected (large) networks. We will also present the use of pattern searching in analysis of genealogies and some approaches to analysis of (multi-relational) temporal networks.
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| 关 键 词: |
pajek; 分析
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-12-22：liyy
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| 阅读次数: |
57
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