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图中节点与其他节点之间的关系

[英]Relation between node to other nodes in a graph

I am simulating a system and in this system there are number of variables that affect each other. 我正在模拟一个系统,并且在这个系统中有许多相互影响的变量。 Despite what or how the effect is, I am presenting this as an undirected, weighted graph where vertices represent these variables and the edges have weights which represent the level of relation between two variables (higher value means higher relation). 尽管效果如何,但我还是将其表示为无向加权图,其中顶点表示这些变量,并且边缘的权重表示两个变量之间的关系水平(值越高,则关系越高)。 In my simulation, I want to choose a node at random (say v ), and check the level of relation between node v and other predefined nodes (say u1, u2, and u3 ) in the network. 在我的模拟中,我想随机选择一个节点(例如v ),并检查节点v与网络中其他预定义节点(例如u1,u2和u3 )之间的关系级别。 For instance, if node v is strongly associated with all of the predefined nodes, then it will get a higher score than the one which is less associated with them. 例如,如果节点v与所有预定义节点紧密关联,则它将获得比与它们较少关联的分数更高的分数。 My question is, if I got the level of association between node v and each node from the predefined nodes ( u1 , u2 , and u3 ) individually and then sum them up. 我的问题是,如果我分别从预定义的节点( u1u2u3 )获得了节点v与每个节点之间的关联级别,然后对其求和。 ie if function f calculates the level of association: 即,如果函数f计算关联级别:

AssociationOf(v) = f(v,u1) + f(v, u2) + f(v, u3) AssociationOf(v)= f(v,u1)+ f(v,u2)+ f(v,u3)

Do you think that this is the right way to do it, given that it is important that the score should reflect the level of association between node v and all predefined nodes TOGETHER? 您认为分数是反映节点v与所有预定义节点TOGETHER之间的关联程度的重要点,您认为这是正确的方法吗? And by together I mean the score needs to reflect the association between node v wrt u1 AND u2 AND u3 (and not u1 OR u2 OR u3). 而且,我的意思是分数需要反映节点v wrt u1和u2 AND u3之间的关联(而不是u1 OR u2 OR u3)。

I hope that it was clear Thank you 我希望很清楚,谢谢

从您的解释“权重代表两个变量之间的关系级别(较高的值表示较高的关系)”,我认为您的函数已经满足了该约束。根据您的拓扑和约束,我看不到任何问题。

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