“…What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. …”

Tabla de Contenidos: “…3.8.1 Computing the Eigenvector Centrality -- 3.9 PageRank Centrality -- 3.9.1 Computing the PageRank Centrality -- 3.10 Hub and Authority -- 3.10.1 Computing the Hub and Authority Centralities -- 3.11 Network Centralities Calculation by Group -- 3.11.1 By Group Network Centralities -- 3.12 Summary -- Chapter 4 Network Optimization -- 4.1 Introduction -- 4.1.1 History -- 4.1.2 Network Optimization in SAS Viya -- 4.2 Clique -- 4.2.1 Finding Cliques -- 4.3 Cycle -- 4.3.1 Finding Cycles -- 4.4 Linear Assignment -- 4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem -- 4.5 Minimum-Cost Network Flow -- 4.5.1 Finding the Minimum-Cost Network Flow in a Demand-Supply Problem -- 4.6 Maximum Network Flow Problem -- 4.6.1 Finding the Maximum Network Flow in a Distribution Problem -- 4.7 Minimum Cut -- 4.7.1 Finding the Minimum Cuts -- 4.8 Minimum Spanning Tree -- 4.8.1 Finding the Minimum Spanning Tree -- 4.9 Path -- 4.9.1 Finding Paths -- 4.10 Shortest Path -- 4.10.1 Finding Shortest Paths -- 4.11 Transitive Closure -- 4.11.1 Finding the Transitive Closure -- 4.12 Traveling Salesman Problem -- 4.12.1 Finding the Optimal Tour -- 4.13 Vehicle Routing Problem -- 4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem -- 4.14 Topological Sort -- 4.14.1 Finding the Topological Sort in a Directed Graph -- 4.15 Summary -- Chapter 5 Real-World Applications in Network Science -- 5.1 Introduction -- 5.2 An Optimal Tour Considering a Multimodal Transportation System - The Traveling Salesman Problem Example in Paris -- 5.3 An Optimal Beer Kegs Distribution - The Vehicle Routing Problem Example in Asheville -- 5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks -- 5.5 Urban Mobility in Metropolitan Cities…”

Tabla de Contenidos: “…Exercises -- 10 Google's Page Rank -- 10.1. Defining the Google matrix -- 10.2. …”

Tabla de Contenidos: “…-- 1.Trust: The Currency of Google -- PageRank -- TrustRank -- Google's Circus -- How to Mine TrustRank -- A Final Word on TrustRank -- 2.The Five Ingredients of Google Optimization -- Ingredient One Keyword Selection -- Take an Informal Survey -- Use the Google AdWords Keyword Planner -- Capitalize on Competitors' Work -- Spend a Few Bucks on a Pay-per-Click Campaign -- Ingredient Two The Meta Page Title -- Maximizing the Effectiveness of Your Meta Page Titles -- Case Study One The Baby Store -- Case Study Two Games -- Ingredient Three Links -- The Psychology of Link Building -- Ingredient Four URL Structure -- Ingredient Five Time -- Final Thoughts -- 3.How to Reel In Links -- Avoiding Bad Neighborhoods -- Link Building 101 -- The Bible of Link Acquisition -- Systematic Emailing -- Link Bait -- 4.Using Time to Gain Trust -- The Sandbox -- Prepublishing Period --…”

“…The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe's economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany."-- Provided by publisher…”

Tabla de Contenidos: “…Sampling by time window -- Extracting audio signatures -- Building a song analyzer -- Selling data science is all about selling cupcakes -- Using Cassandra -- Using the Play framework -- Building a recommender -- The PageRank algorithm -- Building a Graph of Frequency Co-occurrence -- Running PageRank -- Building personalized playlists -- Expanding our cupcake factory -- Building a playlist service -- Leveraging the Spark job server -- User interface -- Summary -- Chapter 9: News Dictionary and Real-Time Tagging System -- The mechanical Turk -- Human intelligence tasks -- Bootstrapping a classification model -- Learning from Stack Exchange -- Building text features -- Training a Naive Bayes model -- Laziness, impatience, and hubris -- Designing a Spark Streaming application -- A tale of two architectures -- The CAP theorem -- The Greeks are here to help -- Importance of the Lambda architecture -- Importance of the Kappa architecture -- Consuming data streams -- Creating a GDELT data stream -- Creating a Kafka topic -- Publishing content to a Kafka topic -- Consuming Kafka from Spark Streaming -- Creating a Twitter data stream -- Processing Twitter data -- Extracting URLs and hashtags -- Keeping popular hashtags -- Expanding shortened URLs -- Fetching HTML content -- Using Elasticsearch as a caching layer -- Classifying data -- Training a Naive Bayes model -- Thread safety -- Predict the GDELT data -- Our Twitter mechanical Turk -- Summary -- Chapter 10: Story De-duplication and Mutation -- Detecting near duplicates -- First steps with hashing -- Standing on the shoulders of the Internet giants -- Simhashing -- The hamming weight -- Detecting near duplicates in GDELT -- Indexing the GDELT database -- Persisting our RDDs -- Building a REST API -- Area of improvement -- Building stories -- Building term frequency vectors…”

Tabla de Contenidos: “…Contents at a Glance; Chapter 1: Machine Learning; Key Terminology; Developing a Learning Machine; Machine Learning Algorithms; Popular Machine Learning Algorithms; C4.5; k -Means; Support Vector Machines; Apriori; Estimation Maximization; PageRank; AdaBoost (Adaptive Boosting); k -Nearest Neighbors; Naive Bayes; Classification and Regression Trees; Challenging Problems in Data Mining Research; Scaling Up for High-Dimensional Data and High-Speed Data Streams; Mining Sequence Data and Time Series Data; Mining Complex Knowledge from Complex Data…”

Tabla de Contenidos: “…; Manual web indexes; Bookmark sites; Evaluation of manual web indexing; Web indexing by search engines; How search engines work; Google's PageRank; What about indexing the 'deep web'?; Notes; References…”

Tabla de Contenidos: “…Social Network Analysis; 7.1 Social Sciences and Bibliometry; 7.2 PageRank and HITS; 7.3 Shortcomings of the Coarse-Grained Graph Model; 7.4 Enhanced Models and Techniques…”

Tabla de Contenidos: “…-- 14.5 Ein Domainkauf -- 14.6 Schlechte Nachbarschaft -- Kapitel 15: Strategische Suchmaschinenoptimierung -- 15.1 Warum Unternehmen im Web meist schlecht dastehen -- 15.2 Content is King -- 15.3 Das Open Directory Project - DMOZ -- 15.4 Auf Einkaufstour für Backlinks -- 15.5 SMO - Social Media Optimization -- 15.6 Beeinflussen Sie den PR: PageRank Sculpting -- 15.7 Der TrustRank -- Kapitel 16: Lösen von technischen Handbremsen…”

Tabla de Contenidos: “…JavaScript FrameworksSummary; Part III: Web Design; Chapter 7: Design Concepts; Design Sense Isn't Innate; The Principles of Design; The Elements of Design; Summary; Chapter 8: User Interface Design; Scanning and Reading; Clear Writing; A Visual Hierarchy; User Testing; Summary; Chapter 9: Search Engine Optimization; The Dominance of Google; PageRank and the Art of Relevance; HTML Optimizations; Google Tools; Summary; Chapter 10: Wireframe Basics; Wireframe Fidelity; Types of Wireframes; Wireframing Tools; Summary; Chapter 11: The Grid; Lay Out the Grid; Grid Tools and Techniques; Summary…”

Tabla de Contenidos: “…Optimizing code quality and load speedMenus, internal navigation, and link structure; Image filenames and alt tags; Text attributes: bold, italics, and underline; Ranking factor: high page count; Fodder for search engines: fresh content; Using the subtle power of outbound links; Understanding off-page ranking factors; Links are the power; Creating natural links; Avoiding over-optimization; Converting visitors to customers: the third spoke of SEO; Creating conversion-based websites; Summary; Chapter 2: Customizing WordPress Settings for SEO…”

Tabla de Contenidos: “…-- Other approaches to community detection -- Summary -- Chapter 10: Supervised Machine Learning on Network Data -- Technical requirements -- Introducing ML -- Beginning with ML -- Data preparation and feature engineering -- Degrees -- Clustering -- Triangles -- Betweenness centrality -- Closeness centrality -- PageRank -- Adjacency matrix -- Merging DataFrames -- Adding labels -- Selecting a model -- Preparing the data -- Training and validating the model -- Model insights -- Other use cases -- Summary -- Chapter 11: Unsupervised Machine Learning on Network Data -- Technical requirements -- What is unsupervised ML? …”

Tabla de Contenidos: “…; RESPOND TO REQUESTS; MOBILIZE YOUR SOCIAL NETWORK; PUT YOUR TWITTER NAME IN YOUR SIGNATURE; RUN A CONTEST; Klout and Page Rank; Chapter 6: The Art of the Tweet; Tweet Etiquette; DON'T SPAM; FOLLOW STYLE RULES; GIVE CREDIT FOR RETWEETS; STICK TO 140 CHARACTERS; FOLLOW PEOPLE WHO FOLLOW YOU; The Benefits of Following before Tweeting; How to Join a Conversation; How to Be Interesting on Twitter…”

Tabla de Contenidos: “…Step 5: Write a Description Meta TagStep 6: Write a Keywords Meta Tag; Step 7: Make Search-Friendly Headlines; Write headlines that pop; Keyphrase headlines early; Step 8: Add Keywords Tactically; Keyphrase anchor text; Buy keyphrased domain names; Step 9: Create Valuable Keyword-Focused Content; Sharpen your keyword-focused content; Create search-friendly URIs; Write compelling summaries; Automatically categorize with blogs; Create tag clouds; Deploy strange attractors; Step 10: Build Inbound Links with Online Promotion; Leverage higher-ranking pages; Don't dilute your PageRank…”

Tabla de Contenidos: “…Calculating the degree of the vertex -- The in-degree -- The out-degree -- Calculating PageRank -- Loading and reloading data about users and followers -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos:

Tabla de Contenidos: “…-- Other NoSQL database systems -- Summary -- Chapter 11: Big Data Analysis with Java -- Scaling, data striping, and sharding -- Google's PageRank algorithm -- Google's MapReduce framework…”

Tabla de Contenidos: “…1.8.1 Linear transformations1.9 Orthogonal Projections; 1.10 Eigenvalues and Eigenvectors; 1.11 Similarity; 1.12 Bezier Curves and Postscript Fonts; 1.12.1 Properties of Bezier curves; 1.12.2 Composite Bezier curves; 1.13 Final Remarks and Further Reading; Exercises; 2 Ranking Web Pages; 2.1 The Power Method; 2.2 Stochastic, Irreducible, and Primitive Matrices; 2.3 Google's PageRank Algorithm; 2.3.1 The personalization vector; 2.3.2 Speed of convergence and sparsity; 2.3.3 Power method and reordering; 2.4 Alternatives to the Power Method; 2.4.1 Linear system formulation…”

Tabla de Contenidos: “…Eigenvector centrality; 1.4.8. Page rank; 1.4.9. Clustering d'un node; 1.5. L'estructura de les xarxes; 1.5.1. …”