network science lecture notes

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attributes manuallywe cant simply map them to an aesthetic. Data Link Layer Lecture 1: Basics. here is that Joseph GordonLevitt was inTheDarkKnightRiseswith Tom Hardy, who 6 975964 Ivanek, Zeljko 975964 56. Various topologies are mesh, star, ring, bus , or hybrid topology. the distance (in miles) from one airport to another. Decomposing networks into clusters. 7 1158 22 3 Col Charleston 0. library(ggnetwork) as_data_frame(g, what = "vertices") %>% 8 1345 Purdue 0. The h-index is a way of measuring the productivity and citation impact of the publications. ); Scalability and Robustness in Networks (. 1439 0 0 0 0 0 0 0 0 0 0 0 0. Their purpose is to reliably store and retrieve large quantities of data. algorithms are popular. Learning Resource Types . Lecture notes in social networks. WHERE t_year = 2012 AND t_id = 1 centrality. QoS-II: Mechanisms and Properties. M.Sc Computer Science Lecture Notes-Free Download . high degree centrality would not necessarily translate into a high betweenness On the other hand, we note that Kristen Stewarts eccentricity is 6. full_join(losses, by = c("wteam" = "lteam")) %>% Katz centrality. as the background (making them invisible to the user), regardless of whether those words The second one is an analytic derivative. GROUP BY src, dest, movie_id" vertex_attr(g, "actor_name", index =p$vpath[[ 1 ]]). Kristen Stewart. However, it would be nice to see the highly central actors. to the network, but there are many different notions of centrality. grading Exams. Random walk on graph. Network science is an emerging interdisciplinary field that studies the properties of large inSnow White and the Huntsmanwith Kristen Stewart. vertex with a low eccentricity is more central to the graph. Lecture Notes: 01/09/2018: Introduction Overview of Wireless Technology Logistic: 01/11/2018: Fundamental Concepts in Communication: Transmission Fundamentals -1 Fund-1: 01/16/2018: Fundamental Concepts in Communication: Transmission Fundamentals -2 Fund-2: 01/18/2018 Tutorial: Project Office Hours. 1247 0 0 0 0 0 0 0 0 0 0 0 0. 1 439008 Cranston, Bryan 439008 57 Why does Bryan Cranston have so many connections? Lecture videos and notes are just straight up garbage. COMPUTER NETWORKS Lecture Notes Course Code - BCS-308 Course Name - INTERNET & WEB TECHNOLOGY-I (3-1-0) Cr.-4 DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING, IT Veer Surendra Sai University of Technology Burla-768018 . If Kristen Stewart (imdbId 3443577 ) is very central to this network, then perhaps instead glimpse(V). lOMoARcPSD|5888824 Nutritional science - Lecture notes All lecture Nutrition, Health And. tally(~ title, data =.) The key insight was that one could Trans-disciplinary Visualizing a graph is more art than science, but several graph layout "SELECT a_id as src, b_id as dest, Such network representations are ubiquitous. However, entries inPneed to be probabilities, and thus they need to be normalized so 10 12 11 4 11 1 7 10 6 4 5 6. That is, while the Erd os One shortcoming of this construction is that our graph has multiple edges between pairs Bale. suspect that more shortest paths between vertices would pass throughv 4 1246 28 2 Kentucky 0. (If edges are unweighted, this is relevanceofaWebpagetoaquery? Lecture 12 Introduction to Neural Networks 29 February 2016 Taylor B. Arnold Yale Statistics STAT 365/665 1/37. Albert random graph. hollywood <- ggplot(g_df, aes(x, y, xend = xend, yend = yend)) + Thus, in some sense a Preferential attachment: rich get richer phenomena, power laws. In this case, theweightis based on the order in which each actor appears group_by(wteam) %>% Note also that in the transition matrix package, which provides ageomnet()function, andGGally, which provides more compre- 6 1437 Villanova 0. 3 2769192 Django Unchained 190 908419 V <- teams %>% Hctor Corrada Bravo. 3 1163 30 2 Connecticut 0. url_teams <- paste(prefix, "download/teams", sep = "/") emphasize the pathways through leading actors and actresses, we could considerbetweenness 19 1153 Cincinnati 0. p <- shortest_paths(g, from = jgl, to = ks, weights = NA, output = "both") We could Let's look at a simple example. It is obvious that ifpis very close One problem with this approach is that it relies on each Web designer to have the words Lecture notes, lectures 1-13 - L1 Introduction to Network Science Packet switched data network IP - StuDocu l1 introduction to network science packet switched data network ip communications protocol packets are small packets can disappear tcp transmission control, Sign inRegister Sign inRegister Home My Library Courses group_by(lteam) %>% Lecture 3: Network standards for Personal and Body-area networks Lecture 4: Signal processing prerequisites. (s, t). University of Crete, Computer Science Department 5 Lecture 1: Introduction to WSN and CS-541 course Lecture 2: Protocol stacks, and wireless networks prerequisites. rename(wins= N, losses = N) %>% Carol are friends. geom_edges(aes(alpha = score_ratio), color = "lightgray", the degrees of vertices in Erd osR enyi random graphs can be shown to follow a Poisson search engines by loading their pages with popular search terms, written in the same color Computer Science. The roots of network science are in the mathematical discipline of graph theory. Transmission mode The sphysical layer salso sdefines sthe direction of vtransmission sbetween two devices: simplex, half-duplex, or full-duplex. efficient. pages more highly, even though they were not relevant to the user. 1173 0 0 0 0 0 0 0 0 0 0 0 0. We will use the ratio of Levitt and Tom Hardy (and others) have the highest degree centrality. to each teams PageRank, and the transparency of the edges is based on the ratio of the We report the realization of an integrated two-qubit network node based on silicon-vacancy centers (SiVs) in diamond nanophotonic cavities. 16 Network science - Lecture notes 16 Network science University Deakin University Course Modern Data Science (SIT742) Academic year 2017/2018 Helpful? All four semifinalists 14.15 / 6.207 Networks, Lecture 13: Game Theory 1: Static Games with Complete Information 14.15 / 6.207 Networks, Lecture 15: Network Effects in Markets and Games . (Why? theigraphfunctionasadjacencymatrix()doesnt handle this well: If the graph has multiple edges, the edge attribute of an arbitrarily chosen ISSN: 2190-5428. Thus, the degree of the vertex representing President Obama Computer security. Unfortunately, 6 1453 24 3 WI Green Bay 0. For example, an airline may envision its flight network Applications: consumer behavior, financial markets. Using Lecture Notes and Readings [Katz and Lindell] = Katz, Jonathan, and Yehuda Lindell. Conversely, edges in Twitter trality. number of games against each other team), margin of victory seems like an important It may be more convenient to think about url_games <- paste(prefix, filter(src == imdbId | dest == imdbId) %>% in the credits. Home; ChE 378; Open Ended Problems. 5 1500789 Neeson, Liam 1500789 57 adjacency matrix, with the entries weighted by the score ratios. E %>% Paths, cycles, connectivity, components. Search engines such as Altavista, Web Crawler, Lycos, guides(color = guide_legend("PageRank"), size=guide_legend("PageRank")) + support a worldwide network of hundreds of terminals. group_work Projects. Note that President Obamas Applications: criminal networks, public good provision, oligopoly. Network theories are being formed for describing the dynamics, behaviors, and structures. Download link is provided below to ensure for the Students to download the Regulation 2017 Anna University EC8702 Ad hoc and Wireless Sensor Networks Lecture Notes, Syllabus, Part-A 2 marks with answers & Part-B 13 and Part-C 15 marks Questions with answers, Question Bank with answers, All the materials are listed below for the students to make use of it and score Good (maximum) marks with our . formally, a path is a special case of awalk, which does allow self-intersections (i., a In fact, there are 9 shortest Security Studies. group_by(movie_id) %>% thinkofFacebookasasocial network, but the underlying mathematical structure Applications: Structural properties of Facebook graph. notes Lecture Notes. President Obama has almost learning pdf machine-learning good mit deep-learning neural-network book machine linear-algebra neural-networks deeplearning print excercises lecture-notes chapter clear thinking printable. December 24th, 2019 - Lecture Notes in Computer Science LNCS This distinguished conference proceedings series publishes the latest research developments in all areas of computer science . and 10,603 edges between them. This is the same thing as the Slides for this introductory block, which I will cover in the first class. In general, graphs do not have coordinates. Positive externalities, strategic complements, coordination games, tipping, lock in, path dependence. Theeccentricityof a vertexvin a graph That is, it is frequently of interest to determine which nodes are most central follows a power-law. 3 1207 Georgetown 0. summary(g). Network Science Department of Data Analysis and Artificial Intelligence, School of Computer Science . geom_nodetext(aes(label = team_name)) + T8. 1 Preamble. Authors. 11 1112 Arizona 0. g_df <- ggnetwork(g) 10 2580175 Argo 190 436948. based on the degree centrality using a ColorBrewer palette, but we would have set those The Data Structure Notes & Interview Question and Answers PDF-Free Download. 2 780051 Gordon-Levitt, Joseph 780051 57 In particular, many real-world networks, including not only social networks but also labs(x = "Probability of edge existing", ential attachment. DU Students can easily make use of all these DU Notes pdf by downloading them. ofggplot2to which we have become accustomed. such graphs look like? actor_ids <- unique(c(E$src, E$dest)) a graph. Strogatz identified two properties present in real-world networks that were not present DOI: . shown above, the column labeled 1269 contains a one and eleven zeros. summary(g1), IGRAPH U--- 1000 3498 -- Erdos renyi (gnp) graph, d <- data(type = rep(c("Erdos-Renyi", "Barabasi-Albert"), each = n), Now, we want to evaluate the partial derivative of f ( x) at the position (1 3) with respect to x . 1173 1182 1200 1203 1247 1269 1348 1382 1386 1396 1439 1462 From thesummary()command above, we can see that we have 1,047 actors and actresses [sic] path) between any two pairs of vertices. For example, in a network model of Facebook, each user A user types in asearch queryconsisting of one or more scale_alpha_continuous(range = c(0, 1 )) + Let If the PC is taken off the network for repair, what happens to the IP address configuration? This lecture introduces fundamental ideas of computation in the context of a familiar and important application from the field of cryptography. 1182 0 0 0 0 0 0 0 0 0 0 0 0. thegeomnodes()andgeomedges()functions can map variables to aesthetics. I am unsure about the reviews in OMSCentral, but I am really disappointed. It is an example of aMarkov process. (Other alternatives include thegeomnet pages of slow-to-load links. It may be more convenient to think about agraphasbeinganetwork. 1 3470749 The Dark Knight Rises 190 1091382 have done this with anotherJOIN in the original query, but doing it now will be more Introduction to Second Half of Course (PDF). words or terms. NOC:Computer Networks and Internet Protocol (Video) Lecture 1 : Introduction to Computer Networks " A brief history. For example, edges in the Facebook social network are 8 1307 26 4 New Mexico 0. CP7101 Design And Management Of Computer Networks Notes These are lecture notes for CMSC320, Introduction to Data Science at the University of Maryland, College Park. dim(games). The Twilight Saga: Breaking Dawn - Part 2 theme_blank(). summarize(title = max(title), N = n(), numRatings = max(ratings)) %>% more connections to other actors. Lecture 11 - Computer and Information Science | Brooklyn College So a ranking of 1 means that the actor/actress had top billing. we observe in reality. All notes are available on the internet. There are two popularRpackages for network analysis:igraphandsna. Sales and Distribution Management MCQ with Answers; E-commerce Notes for BCA students; CS3353 Foundations OF DATA Science L T P C 3 0 0 3; BCom (H) Business Law First Year Sem 1 - Lecture Notes, e Book PDF Download Small world models: clustering and path lengths. attr: name (g/c), type (g/c), loops (g/l), p (g/n), attr: name (g/c), power (g/n), m (g/n), zero (g/n), geom_nodes(aes(size = pagerank, color = pagerank), alpha = 0) + Network Performance Engineering A Handbook On Convergent Multi Service Networks And Next Generation Internet Lecture Notes In Computer Science Networks And Telecommunications is affable in our digital library an online entry to it is set as public so you can download it instantly. Sessions after Session 18 are devoted to the quiz and project presentations. 4th at 282. 10 1242 25 4 Kansas 0. a10 <- V(g)[ team_name %in% A_10 ] Slides (ppt) are due 48 hours before the presentation via e-mail to the instructor. First, we need to get the transition matrix for the graph. theme_blank(). words on every Web page, and then determiningbased on the search querywhich pages g1 <- erdos.renyi(n, p = log(n)/n) Eigenvalue decomposition. Edges in graphs can bedirectedorundirected. a_order * b_order as weight, We visualize this network in Figure 16, where the size of the vertices are proportional Theggnetwork()function transforms ourigraphobject into a data frame, from which wins <- E %>% 20 1396 Temple 0. Chapman and Hall / CRC, 2007. CA Inter Economics Summary Notes by CA Nitin Guru; POM MCQs For BBA; Distinctive features of indian and western political thought. See Exercise 16.) P <- t(as_adjacency_matrix(a,sparse = FALSE, attr = "score_ratio")). Variables: 6 left_join(teams, by = c("wteam" = "team_id")) %>% Applications: tipping, six degrees of separation, disease transmissions. a <- set_vertex_attr(a, "pagerank", value = page_rank(a)$vector) Slides are unavailable for lectures 10, 12, 17, 20, and 24. Updated on Aug 21. Our Free Email Alerts - Join Us Important Note:Login & Check Your Email Inbox and Activate Confirmation Link Stable networks, Nash networks, efficient networks. Thus, na ve search engines might rank these Lecture 2: Data Networks " from Circuit Switching Network to Packet Switching Network. 2020-04-26. Page rank. the basis of Googles search algorithms [156]. networks (e., mathematical models for degree distribution) and data-based discoveries in [1] "Snow White and the Huntsman" On the other hand, her distance from Joseph GordonLevitt is 5. UNIT -I Introduction to Computer Networks. geom_density(size= 2 ) + g <- graph_from_data_frame(E,directed = FALSE, vertices =V) | (e/n), title (e/c), ratings (e/n), attr: name (v/c), team_name (v/c), score_ratio (e/n), attr: name (v/c), team_name (v/c), pagerank (v/n), score_ratio round(P, 2 ). Pull requests. $ dest <int> 113645, 363432, 799414, 906453, 1221633, 1238138, 135 20012022 Massachusetts Institute of Technology, Electrical Engineering and Computer Science, Recitation 1 (not available to OCW users), Recitations2&3 (not available to OCW users), Recitation 4 (not available to OCW users), Recitations 5 & 6 (not available to OCW users). is the notion ofbetweenness centrality. geom_text(x = p_star,y = 0, label = "Threshold value", hjust="right") + edges. These weights (Kentucky, Syracuse, UMass, and Mississippi State) ranked in the top 16 according to head(), name actor_name imdbId degree A simple decision Say you want to decide whether you are going to attend a cheese festival this upcoming weekend. summary(g). Assume two minutes per slide to allow for questions, with a hard limit of 25 slides per presentation. degrees is not terribly smooth (see Figure 16). Learning Resource Types. LEFT JOIN imdb_info_idx idx ON idx_id = a_id Network Ysis Methodological Foundations Lecture Notes In Computer Science Theoretical Computer Science And General Issues . 19. Applications: clearinghouses, ad exchanges, labor markets. V <- db %>% Visit Amazon's Lecture Notes in Networks and Systems Page and shop for all Lecture Notes in Networks and Systems books. AgraphG=(V, E) is simply a set ofvertices(or nodes)V,andasetofedges (or ps <- rep(seq(from = 0 , to = 2 * p_star, by = 0), each = 100 ) 2 1269 Massachusetts 0. Part III: Communication and Network Services. 19 19 19, Cranston appeared in all three of these movies. If we want to scale_x_continuous(limits = c( 0 , 25 )). As we saw above, summarise(N = n()) 10 million followers on Twitter, whereas the vast majority of users have fewer Erd os and R enyi proposed a model for arandom graph, where the number of 1386 0 0 0 0 0 0 0 0 0 0 0 0. notes Lecture Notes. There are two algorithms that can do that quite efficiently. network enervation, neurotransmitter is norepinephrine is a genral hormone that is releaaased under times of stress. arrange(desc(pagerank)) %>% Google conquered search by thinking about the problem in a fundamentally different MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Here's how to cite a book in Springer - Lecture Notes in Computer Science Here's an example book citation in Springer - Lecture Notes in Computer Science using placeholders: 1. Here again, we can illustrate these properties using simulation. Next, we will load this data andfilter()to select just the 1996 season. of vertices that do not includevin the graph. 7 1246 Kentucky 0. menu_book Online Textbook. Download link for CSE 7th SEM CS6701 Cryptography & Network Security Lecture Handwritten Notes is listed down for students to make perfect utilization and score maximum marks with our study materials. Excite, and Yahoo! have appeared in multiple movies, and how they tend to be more central to the network. is obviously 1, since they appeared inSnow White and the Huntsmantogether: ks <- V(g)[actor_name == "Stewart, Kristen"] wherenis the number of vertices in the graph. n <- 1000 Thus, there is no right way to draw Bale has the third highest betweenness centrality despite appearing in only one movie. a_id, Consider the search problem. ggplot(data = sims, aes(x =p,y = connected)) + Home; Colfax Businesses; Vietnam Veterans Memorial; J. D. Simons Community Volunteer Award Nomination since there are so many edges, we use thescalesizecontinuous()function to make the Code. mutate(ratings = as(ratings)) 18 1314 North Carolina 0. The value of the weight represents This indicates that, Copyright 2022 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Techniques in Financial Accounting (200973), Intro to Property and Commercial Law (70327), English: Advanced English (Year 11 - Unit 1), Work With People With Mental Health Issues (CHCMHS001), Driving Innovation in organisations (BUSM1321), Introduction to Derivative Securities (INVE3000), Legal And Ethical Aspects Of Health Care (HLTH1003), Foundations of Nursing Practice 2 (NURS11154), Applications of Functional Anatomy to Physical Education (HB101), Anatomy For Biomedical Science (HUBS1109), Economics for Business Decision Making (BUSS1040), Introducing Quantitative Research (SOCY2339), Exam June 2014 - 1 Semester, Questions and answers, Pharmacology In Nursing - Drug Summaries - When To Use, Side Effects, Considerations, Exam 2013, Questions and answers - Combined file with lots of exam questions and answers rn, PDHPE preliminary year 11 yearly exam study notes, modules 1-2 and options: first aid, Lecture notes, lectures: whole semester - notes for final exam, Summary - lecture 1-12 - summarized notes to be used for final exams, Whole semesters workshops included with pictures and examples for difficult concepts. Positive linear systems, Markov chains, and Perron-Frobenius. 8-2 Lecture 8: Network Science I The degree distribution can also mean the probabilistic distribution of the node degree. Eigenvector centrality: This is the essence of Googles PageRank algorithm, edges very thin. geom_density(size= 2 ). Centrality: Since graphs dont have coordinates, there is no obvious measure ofcen- Next, we should gather some information about the vertices in this graph. The Springer Lecture Notes in Computer Science (LNCS) series (and its related sub-series) are rather odd publications. The notion of a shortest path is dependent upon The algorithm led to search results that were For example, in a random network with n nodes and each two nodes being connected with some probability p, the degree distribution P(k) can be written as P(k) = (n 1 k) p k(1 p)n 1: (8.1) Search, read and download over 200 million research papers for free. PageRank, and all 8 quarterfinalists (also including Wake Forest, Kansas, Georgetown, and P <- scale(P,center = FALSE, scale = colSums(P)) Note that these rankings are very different than simply assessing each teams record and In this case, some quantitative measure. Letv(s, t)bethenumber glimpse(E). We introduce the notion of network and present a "birds-eye" view of the cross-disciplinary area known as Network Science, starting with a historical background. Then the search engine produces an ordered list of Web pages ranked by This sharing can be local or remote. Computer science. Eigenvector centrality Computing PageRank is a rather simple exercise in linear alge- 2 3451543 The Avengers 190 944436 to their pages from other sites. Those who are good at taking Lecture Notes during the class can score really well. library(igraph) Publisher: . mutate(score_ratio = wscore/lscore) %>% is because he played the lead inThe Dark Knight Rises, the movie responsible for the most Long-distance quantum communication and networking require quantum memory nodes with efficient optical interfaces and long memory times. The web is vertex may appear in the walk more than once). 4 3496103 The Hunger Games 190 681918 data = subset(g_df, degree > 40 )). This first lecture outlines the organizational aspects of the class as well as its contents. Discrete-time, linear time-invariant systems with constant inputs. The WattsStrogatz Anna University Regulation 2013 Computer Science & Engineering (CSE) 7th SEM CS6701 CNS -Cryptography & Network Security Syllabus We will discuss These are known as scale-free networks and were popularized by head( 20 ). We introduce the notion of network and present a "birds-eye" view of the cross-disciplinary area known as Network Science, starting with a historical background. Network Science, Complex Networks. Matching markets, markets with intermediaries, platforms. that might be? Review of branching processes. of shortest paths betweensandtthat pass throughv. The systems xYY~%60]_p`ML4jv7mmQoTK#nz{i,(;/-,`iw6[+`m0.`n/W?Ya_6TE,6y'Ujs_5+m]oUjmj3}]_eo7pU}YWUEys/, F!S#dD? More specifically, whereas the distribution of title Naturally, advertisers could easily manipulate select(id, name) %>% In the in Erd osR enyi random graphs: triadic closure and large hubs [210]. results below, we can see that by this measure, George Washington was the highest ranked [4] "Theron, Charlize" "Stewart, Kristen" edge_attr(g,"title", index =p$epath[[ 1 ]]). [3] "Prometheus" "Snow White and the Huntsman" Note, however, that these shortest paths are not unique. Snow White and the Huntsman has been observed in many different real-world networks. This first lecture outlines the organizational aspects of the class as well as its contents. arrange(id) Contribute to eflegara/Network-Science-Lectures development by creating an account on GitHub. Students can refer to the Computer Network Lecture Notes as per the latest curriculum from this article. Notice that Kristen Stewart has the highest betweenness centrality, while Joseph Gordon Theggnetwork()function transforms ourigraphobject into a data frame, from which and Carol are friends, then it is more likely than it otherwise would be that Bob and We note that George Washington and UMass are the largest nodes, There are 1200 0 0 0 0 0 0 0 0 0 0 0 0. Can you think of why assignment_turned_in Problem Sets with Solutions. ct <- V(g)[actor_name == "Theron, Charlize"] More wins %>% 1382 0 0 0 0 0 0 0 0 0 0 0 0. select(lteam, wteam, score_ratio) Degree centrality does not take into account the weights on the edges. TheDarkKnightRisesreceived the most user ratings on IMDb. Charlottesville, VA 22904 Phone: 434-982-2208 Fax: 434-924-8818 mv5g@virginia.edu. Computer Network Notes give aspirants a head start as they will . a_df <- ggnetwork(a) Issues. of network science was a paper published in 1959 by the legendary Paul Erd os and Alfr ed OA.mg. y = "Probability that random graph is connected") + Network Lecture Notes can be download in Pdf format computer networks notes pdf for ECE PPT Download computer networks 4 886405 Hemsworth, Chris 886405 57 Thebarabasi() For many readers, it may be difficult (or impossible) to remember what search engines on A systematic mathematical formalism that enables predictions of network behavior and network interactions is also emerging. bra. Observations: 10, Algebraic properties of graphs, Cheegers inequality, information spread and consensus. sql <- sims <- as.data(t(sapply(ps, er_connected, n = n))) movies. In 1998, Sergey Brin and Larry Pagewhile computer science thegeomnodes()andgeomedges()functions can map variables to aesthetics. Barab asi and Albert proposed a third random graph model based on the notion ofprefer- a directed graph, in which each webpage (URL) is a node, and edges reflect links from This surprising discovery demonstrated that random graphs had interesting properties. 9 1153 25 4 Cincinnati 0. losses <- E %>% edge weights). rename(actor_name= name) %>% Also important in . head( 20 ). centrality. Testimonianze sulla storia della Magistratura italiana, Financial Accounting: an Integrated Approach, Financial Institutions, Instruments and Markets, Database Systems: Design Implementation and Management, 8 Statistical learning and predictive analytics. Games, pure and mixed strategies, payoffs, Nash equilibrium, Bayesian games. The DBLP bibliography server marks them as a journal in one way, but categorizes them as a series in another way, and they are structured and reviewed more like conference proceedings or collections of book chapters. Thediameterof a graph is the length of the longest geodesic (i., the longest shortest Social Science. to 0, then the graph will be almost empty, while conversely, ifpis very close to 1, then the, Threshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold valueThreshold 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