挖掘社交网络（第2版 影印版）
《挖掘社交网络（第2版 影印版）》中简洁而且具有操作性的书将为你展示如何回答这些甚至更多的问题，你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你找到你一直在社交世界中的内容。《挖掘社交网络（第2版 影印版）》中每个独立章节介绍了在社交网络的不同领域挖掘数据的技术，这些领域包括博客和电子邮件。你所需要具备的就是一定的编程经验和学习基本的python工具的意愿。
简介
《挖掘社交网络（第2版 影印版）》中简洁而且具有操作性的书将为你展示如何回答这些甚至更多的问题，你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你找到你一直在社交世界中的内容。《挖掘社交网络（第2版 影印版）》中每个独立章节介绍了在社交网络的不同领域挖掘数据的技术，这些领域包括博客和电子邮件。你所需要具备的就是一定的编程经验和学习基本的python工具的意愿。
版权
出版社东南大学出版社
出版时间2014年10月
分类计算机-计算机综合
热门划线
挖掘社交网络（第2版 影印版）
[美]罗塞尔
- Preface
- PartⅠ.A Guided Tour ofthe SociaIWeb
- Prelude
- 1.1.Overview
- 1.2.Why Is Twitter All the Rage？
- 1.3.Exploring Twitter s API
- 1.3.1.Fundamental Twitter Terminology
- 1.3.2.Creating a Twitter API Connection
- 1.3.3.Exploring Trending Topics
- 1.3.4.Searching for Tweets
- 1.4.Analyzing the 140 Characters
- 1.4.1.Extracting Tweet Entities
- 1.4.3.Computing the Lexical Diversity of Tweets
- 1.4.4.Examining Patterns in Retweets
- 1.4.5.Visualizing Frequency Data with Histograms
- 1.5.Closing Remarks
- 1.6.Recommended Exercises
- 1.7.Online Resources
- 2.1.Overview
- 2.2.Exploring Facebook s Social Graph API
- 2.2.1.Understanding the Social Graph API
- 2.2.2.Understanding the Open Graph Protocol
- 2.3.Analyzing Social Graph Connections
- 2.3.1.Analyzing Facebook Pages
- 2.3.2.Examining Friendships
- 2.4.Closing Remarks
- 2.5.Recommended Exercises
- 2.6.OnlLne Resources
- 3.1.Overview
- 3.2.Exploring the Linkedln API
- 3.2.1.Making Linkedln API Requests
- 3.2.2.Downloading Linkedln Connections as a CSV File
- 3.3.Crash Course on Clustering Data
- 3.3.1.Clustering Enhances User Experiences
- 3.3.2.Normalizing Data to Enable Analysis
- 3.3.3.Measuring Similarity
- 3.3.4.Clustering Algorithms
- 3.4.Closing Remarks
- 3.5.Recommended Exerases
- 3.6.Online Resources
- 4.1.Overview
- 4.2.Exploring the Google+ API
- 4.2.1.Making Google+ API Requests
- 4.3.A Whiz—Bang Introduction to TF—IDF
- 4.3.1.Term Frequency
- 4.3.2.Inverse Document Frequency
- 4.3.3.TF—IDF
- 4.4.Querying Human Language Data with TF—IDF
- 4.4.1.Introducing the Natural Language Toolkit
- 4.4.2.Applying TF—IDF to Human Language
- 4.4.3.Finding Similar Documents
- 4.4.4.Analyzing Bigrams in Human Language
- 4.4.5.Reflections on Analyzing Human Language Data
- 4.5.Closing Remarks
- 4.6.Recommended Exercises
- 4.7.Online Resources
- 5.1.Overview
- 5.2.Scraping， Parsing， and Crawling the Web
- 5.2.1.Breadth—First Search in Web Crawling
- 5.3.Discovering Semantics by Decoding Syntax
- 5.3.2.Sentence Detection in Human Language Data
- 5.3.3.Document Summarization
- 5.4.Entity—Centric Analysis： A Paradigm Shift
- 5.4.1.Gisting Human Language Data
- 5.5.Quality ofAnalytics for Processing Human Language Data
- 5.6.Closing Remarks
- 5.7.Recommended Exercises
- 5.8.Online Resources
- 6.1.Overview
- 6.2.Obtaining and Processing a Mail Corpus
- 6.2.1.A Primer on Unix Mailboxes
- 6.2.2.Getting the Enron Data
- 6.2.3.Converting a Mail Corpus to a Unix Mailbox
- 6.2.4.Converting Unix Mailboxes to JSON
- 6.2.5.Importing a JSONified Mail Corpus into MongoDB
- 6.2.6.Programmatically Accessing MongoDB with Python
- 6.3.Analyzing the Enron Corpus
- 6.3.1.Querying by Date／Time Range
- 6.3.3.Writing Advanced Queries
- 6.3.4.Searching Emails by Keywords
- 6.4.Discovering and Visualizing Time—Series Trends
- 6.5.Analyzing Your Own Mail Data
- 6.5.1.Accessing Your Gmail with OAuth
- 6.5.2.Fetching and Parsing Email Messages with IMAP
- 6.6.Closing Remarks
- 6.7.Recommended Exercises
- 6.8.Online Resources
- 7.1.Overview
- 7.2.Exploring GitHub s API
- 7.2.1.Creating a GitHub API Connection
- 7.2.2.Making GitHub API Requests
- 7.3.Modeling Data with Property Graphs
- 7.4.Analyzing GitHub Interest Graphs
- 7.4.1.Seeding an Interest Graph
- 7.4.2.Computing Graph Centrality Measures
- 7.4.4.Using Nodes as Pivots for More Efflcient Queries
- 7.4.5.Visualizing Interest Graphs
- 7.5.Closing Remarks
- 7.6.Recommended Exercises
- 7.7.Online Resources
- 8.1.Overview
- 8.2.Microformats： Easy—to—Implement Metadata
- 8.2.2.Using Recipe Data to Improve Online Matchmaking
- 8.2.3.Accessing Linkedln s 200 Million Online Resumes
- 8.4.The Semantic Web： An Evolutionary Revolution
- 8.4.1.Man Cannot Live on Facts Alone
- 8，4.2.Inferencing About an Open World
- 8.5.Closing Remarks
- 8.6.Recommended Exercises
- 8.7.Online Resources
- PartⅡ.Twitter（ookbook
- 9.TwitterCookbook
- 9.1.Accessing Twitter s API for Development Purposes
- 9.3.Discovering the Trending Topics
- 9.4.Searching for Tweets
- 9.5.Constructing Convenient Function Calls
- 9.6.Saving and Restoring JSON Data with Text Files
- 9.7.Saving and Accessing JSON Data with MongoDB
- 9.8.Sampling the Twitter Firehose with the Streaming API
- 9.9.Collecting Time—Series Data
- 9.10.Extracting Tweet Entities
- 9.13.Tabulating Frequency Analysis
- 9.14.Finding Users Who Have Retweeted a Status
- 9.15.Extracting a Retweet s Attribution
- 9.16.Making Robust Twitter Requests
- 9.17.Resolving User Profile Information
- 9.18.Extracting Tweet Entities from Arbitrary Text
- 9.19.Getting All Friends or Followers for a User
- 9.20.Analyzing a User s Friends and Followers
- 9.21.Harvesting a User s Tweets
- 9.22.Crawling a Friendship Graph
- 9.23.Analyzing Tweet Content
- 9.24.Summarizing Link Targets
- 9.25.Analyzing a User s Favorite Tweets
- 9.26.Closing Remarks
- 9.27.Recommended Exercises
- 9.28.Online Resources
- PartⅢ.Appendixes
- A.Information About This Book s Virtual Machine Experience
- B.OAuth Primer
- C.Python and I Python Notebook Tips & Tricks
- Index.