could you answer the question please? 4.2. Friendship ParadoxThe degree distribution p, expresses the probability that a randomlyselected node has k neighbors. However, if we randomly select a link, theprobability that a node at one of its ends has degree k is q, = Akp, where Ais a normalization factor.(a) Find the normalization factor A, assuming that the network hasa power law degree distribution with 2 < y < 3, with minimumdegree kin and maximum degree kmax'(b) In the configuration model q, is also the probability that a ran-domly chosen node has a neighbor with degree k. What is the av-erage degree of the neighbors of a randomly chosen node?(c) Calculate the average degree of the neighbors of a randomly cho-sen node in a network with N = 104, y= 2.3, kmin=1 and k=1, 000.%3D%3D%3DmaxCompare the result with the average degree of the network, (k).(d) How can you explain the "paradox" of (c), that is a node's friendshave more friends than the node itself?

4.2. Friendship Paradox The degree distribution p, expresses the probability that a randomly selected node has k neighbors. However, if we randomly select a link, the probability that a node at one of its ends has degree k is q, = Akp, where A %3D is a normalization factor. (a) Find the normalization factor A, assuming that the network has

4.2. Friendship Paradox The degree distribution p, expresses the probability that a randomly selected node has k neighbors. However, if we randomly select a link, the probability that a node at one of its ends has degree k is q, = Akp, where A %3D is a normalization factor. (a) Find the normalization factor A, assuming that the network has

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Consider the following network: (a) Which of the following most accurately describes the connectedness of this network? Strongly Weakly Disconnected None of the above (b) When discussing path lengths on a weighted graph, one must first define how the weights are related to the length of a path between two nodes is then the sum of the distances of the links in that path. Consider the previous network and assume that the link weights represent distances. Using this distance metric, what is the shortest path between nodes 1 and 6? (c) A common way to define the distance between two nodes is the inverse (or reciprocal) of the link weight. Consider the previous network and assume that the distance between two adjacent nodes is defined as the reciprocal of the link weight. Using this distance metric, what is the shortest path between node 1 and 6?
Consider the following network: a.) Find the shortest path from node 1 to node 8 using Dijkstra’s algorithm. Write the length of the shortest path as well as the edges that compose it. b.) Write an integer programming formulation to find the second-shortest path from 1 to 8 in this network. (Hint: if the shortest path is not available, the second-shortest path becomes the shortest path.)
Consider the network in the following figure. Edges that are not pictured have a length of ∞. Image attatched (a) What is the optimal TSP tour on this network, and what is its total length, z*? What is the optimal 1-tree Tˆ* on this network (“rooted” at node 1), and what is its total length, z(Tˆ*)? (b) Find the best Held-Karp lower bound you can. That is, find weights to add to one or more nodes so that z* and the right-hand side of the following formula are as close as possible. z* ≥ z(Tˆ*) + SUM{i∈N} λi(di(Tˆ*) − 2) Image attatched Here Tˆ* is the optimal 1-tree under the revised distance matrix, z(Tˆ*) is its cost and di(Tˆ*) is the degree of node i in 1-tree Tˆ*.
Let A, B, C, D be the vertices of a square with side length 100. If we want to create a minimum-weight spanning tree to connect these four vertices, clearly this spanning tree would have total weight 300 (e.g. we can connect AB, BC, and CD). But what if we are able to add extra vertices inside the square, and use these additional vertices in constructing our spanning tree? Would the minimum-weight spanning tree have total weight less than 300? And if so, where should these additional vertices be placed to minimize the total weight? Let G be a graph with the vertices A, B, C, D, and possibly one or more additional vertices that can be placed anywhere you want on the (two-dimensional) plane containing the four vertices of the square. Determine the smallest total weight for the minimum-weight spanning tree of G. Round your answer to the nearest integer. Note: I encourage you to add n additional points (for n=1, 2, 3) to your graph and see if you can figure out where these point(s) need to…
Let A, B, C, D be the vertices of a square with side length 100. If we want to create a minimum-weight spanning tree to connect these four vertices, clearly this spanning tree would have total weight 300 (e.g. we can connect AB, BC, and CD). But what if we are able to add extra vertices inside the square, and use these additional vertices in constructing our spanning tree? Would the minimum-weight spanning tree have total weight less than 300? And if so, where should these additional vertices be placed to minimize the total weight? Let G be a graph with the vertices A, B, C, D, and possibly one or more additional vertices that can be placed anywhere you want on the (two-dimensional) plane containing the four vertices of the square. Determine the smallest total weight for the minimum-weight spanning tree of G. Round your answer to the nearest integer. Attention: Please don't just copy these two following answers, which are not correct at all. Thank you.…
Suppose there is undirected graph F with nonnegative edge weights we ≥ 0. You have also calculated the minimum spanning tree of F and also the shortest paths to all nodes from a particular node p ∈ V . Now, suppose that each edge weight is increased by 1, so the new weights are we′ = we + 1. (a) Will there be a change of the minimum spanning tree? Provide an example where it does or prove that it cannot change. (b) (3 points) Will the shortest paths from p change? Provide an example where it does or prove that it cannot change.
Consider the Omega network and Butterfly network from p nodes in the leftmost column to p nodes in the rightmost column for some p=2^k. The Omega network is defined in Chapter 2 of the text book such that Si is connected to element S j if j=2i for or j=2i+1-p for See Chapter 2 in text book for its definition. The Butterfly network is an interconnection network composed of log p levels (as the omega network). In a Butterfly network, each switching node i at a level l is connected to the identically numbered element at level l + 1 and to a switching node whose number differs from itself only at the lth most significant bit. Therefore, switching node Si is connected to element S j at level l if j = i or j . Prove that for each node Si in the leftmost column and a node Sj in the rightmost column, there is a path from Si to Sj in the Omega network. Prove that for each node Si in the leftmost and a node Sj in the rightmost, there is a path from Si to Sj in the Butterfly network.
Consider a prize-collecting TSP we discussed as the subproblem for VRP: there is no constraint requiring every node to be on the tour, but there is a reward πi for visiting node i. The objective is to minimize the total tour length minus the total rewards for nodes visited. (a) Formulate the prize-collecting TSP as a linear integer programming problem. (b) Propose a heuristic for the prize-collecting TSP. Explain your heuristic in word. This question is open-ended.
a) Draw the connected subgraph of the given graph above which contains only four nodes ACGB and is also a minimum spanning tree with these four nodes. What is its weighted sum? Draw the adjacency matrix representation of this subgraph (use boolean matrix with only 0 or 1, to show its adjacency in this case).b) Find the shortest path ONLY from source node D to destination node G of the given graph above, using Dijkstra’s algorithm. Show your steps with a table as in our course material, clearly indicating the node being selected for processing in each step.c) Draw ONLY the shortest path obtained above, and indicate the weight in each edge in the diagram. Also determine the weighted sum
where a is the start node and e is the goal node. The pair [f, h] at each node indicates the value of the f and h functions for the path ending at that node. Given this information, what is the cost of each path? The cost < a, c >= 2 is given as a hint. Is the heuristic function h admissible? Explain
You are given a graph G = (V, E) with positive edge weights, and a minimum spanning tree T = (V, E') with respect to these weights; you may assume G and T are given as adjacency lists. Now suppose the weight of a particular edge e in E is modified from w(e) to a new value w̃(e). You wish to quickly update the minimum spanning tree T to reflect this change, without recomputing the entire tree from scratch. There are four cases. In each case give a linear-time algorithm for updating the tree. Note, you are given the tree T and the edge e = (y, z) whose weight is changed; you are told its old weight w(e) and its new weight w~(e) (which you type in latex by widetilde{w}(e) surrounded by double dollar signs). In each case specify if the tree might change. And if it might change then give an algorithm to find the weight of the possibly new MST (just return the weight or the MST, whatever's easier). You can use the algorithms DFS, Explore, BFS, Dijkstra's, SCC, Topological Sort as…
What are the three prerequisites for a network to function optimally? Let's break them down and examine them separately.