PR(t) Consider the simple PageRank definition. Let N(v) denote the set of inlink neighbors of v,...

 

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PR(t) Consider the simple PageRank definition. Let N(v) denote the set of inlink neighbors of v, C(v) denote the outdegree of u, then the PageRank of v is PR(v) = [teN(v) (1) (a) (8 points) Following the above definition, please fill in the table below to show the computed PageRank score of each node in the following network for the first two iterations. Note that PR(v) is initialized as 1. Node A B D XI PageRank Score Initialization Iteration 1 Iteration 2 (b) (4 points) In the above network, node D is a dangling node. How can we address the spider trap problem? For example, eventually PR(D) keeps accumulating to 4 while other nodes' scores tend to be zero. PR(t) Consider the simple PageRank definition. Let N(v) denote the set of inlink neighbors of v, C(v) denote the outdegree of u, then the PageRank of v is PR(v) = [teN(v) (1) (a) (8 points) Following the above definition, please fill in the table below to show the computed PageRank score of each node in the following network for the first two iterations. Note that PR(v) is initialized as 1. Node A B D XI PageRank Score Initialization Iteration 1 Iteration 2 (b) (4 points) In the above network, node D is a dangling node. How can we address the spider trap problem? For example, eventually PR(D) keeps accumulating to 4 while other nodes' scores tend to be zero.