Pagerank个性化向量，边缘权重和悬空字典(传送向量) [英] Pagerank Personalization vector , edge weights and dangling dictionary (teleportation vector)

问题描述

这是networkx的Pagerank函数

This is the pagerank function from networkx

def pagerank(G, alpha=0.85, personalization=None,
             max_iter=100, tol=1.0e-6, nstart=None, weight='weight',
             dangling=None):

我对个性化和体重感到困惑.

I am confused with personalization and weight.

我了解何时不使用个性化矩阵提供统一矩阵，何时不提供权重使用边缘权重1.

I understand the when personalization matrix is not provides a uniform matrix is used and when weight is not provided edge weight of 1 is used.

我一直在阅读:边缘权重个性化和节点权重个性化.

I have been reading about :Edge weight personalization and Node Weight Personalization.

http://www.cs .cornell.edu/〜bindel/present/2015-08-kdd-talk_kdd-aug15.pdf

所以我的假设是个性化矢量->可用于节点权重个性化.例如.我们正在计算特定主题t的节点的pagerank.我们提供个性化矢量，使与该主题相关性更高的节点获得更多价值.

So my assumption is personalization vector --> can be used for node weight personalization. for example. we are calculating pagerank of nodes for particular topic t . we give personalization vector where nodes more relevant to the topic get more value.

但是边缘重量呢?当我将V的一个输出边缘的权重赋予更高的权重时，会发生什么. ?

But what about edge weight. What happens when I give higher weight to one outgoing edge from V as compared to other. ?

我无法区分这两件事(边缘权重和个性化向量).

I am not able to segregate these two things (edge weights and personalization vector) .

，还有: 字典dangling:代表隐形传态向量.如果未提供，则使用个性化矢量.悬空字典对我来说更容易理解，即当我们到达悬空节点时，它提供了随机过渡的可能性.

and also : the dictionary dangling : which represents the teleportation vector. If not provided personalization vector is used. The dangling dictionary is more understandable to me , that is provides the probability of random transition when we reach a dangling node.

有人可以帮助我了解我提供这三种情况的情况，即边缘权重，个性化向量和悬挂向量(pagerank将如何受到影响)

Could someone help be understand the scenario where I provide all three , i.e edge weight, personalization vector and Dangling vector ( How will pagerank be affected)

推荐答案

这实际上不是编程问题，但我还是会回答.

This isn't really a programming question but I'll answer anyway.

在NetworkX实现中，如果设置个性化矢量，则这些值也将用于悬挂节点"-您不能单独设置悬挂节点的值.

In the NetworkX implementation if you set a personalization vector those values will also used for the "dangling nodes" - you cannot set the dangling node values separately.

'weight ='参数给出edge属性，用作数字边缘权重.默认值为weight = None，将所有权重设置为相等(等于1).如果要偏置某些边缘，可以根据图形中的属性随意调整它们.

The 'weight=' parameter gives the edge attribute to be used as a numeric edge weight. The default with weight=None sets all of the weights equal (to 1). You can adjust them as you like according to properties in your graph if you want to bias certain edges.

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