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20240809213013.0 |
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m o d |
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150131s2015 xx o 000 0 eng d |
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|a EBLCP
|b eng
|e pn
|c EBLCP
|d OCLCQ
|d DEBSZ
|d OCLCQ
|d K6U
|d OCLCO
|d IDB
|d OCLCF
|d COCUF
|d CCO
|d PIFFA
|d FVL
|d ZCU
|d MERUC
|d OCLCQ
|d VT2
|d OCLCO
|d U3W
|d STF
|d ICG
|d INT
|d OCLCQ
|d TKN
|d OCLCQ
|d DKC
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|d OCL
|d OCLCQ
|d OCLCO
|d OCLCL
|d UEJ
|d OCLCQ
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| 020 |
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|a 9781118845691
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|a 1118845692
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|a (OCoLC)902417360
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|a T57.85
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|a HCDD
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|a Brath, Richard.
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| 245 |
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|a Graph Analysis and Visualization :
|b Discovering Business Opportunity in Linked Data.
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| 260 |
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|a Hoboken :
|b Wiley,
|c 2015.
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| 300 |
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|a 1 online resource (539 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 588 |
0 |
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|a Print version record.
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|a Introduction; Part 1: Overview; Chapter 1: Why Graphs?; Visualization in Business; Graphs in Business; Finding Anomalies; Managing Networks and Supply Chains; Identifying Risk Patterns; Optimizing Asset Mix; Mapping Social Hierarchies; Detecting Communities; Graphs Today; Summary; Chapter 2: A Graph for Every Problem; Relationships; Hierarchies; Communities; Flows; Spatial Networks; Summary; Part 2: Process and Tools ; Chapter 3: Data-Collect, Clean, and Connect; Know the Objective; Collect: Identify Data; Potential Graph Data Sources; Potential Hierarchy Data Sources; Getting the Data.
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|a Clean: Fix the DataConnect: Organize Graph Data; Compute the Graph; Graph Data File Formats; Putting It All Together; Summary; Chapter 4: Stats and Layout; Basic Graph Statistics; Size (Number of Nodes and Number of Edges); Density; Number of Components; Degree and Paths; Centrality; Viral Marketing Example; Layouts; Node-and-Link Layouts; Other Layouts; Force-Directed Layout; Node-Only Layout; Time Oriented; Top-Down and Other Orthogonal Hierarchies; Radial Hierarchy; Geographic Layout and Maps; Chord Diagrams; Adjacency Matrix; Treemap; Hierarchical Pie Chart; Parallel Coordinates.
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8 |
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|a Putting It All TogetherSummary; Chapter 5: Visual Attributes; Essential Visual Attributes; Key Node Attributes; Node Size; Node Color; Labels; Key Edge Attributes; Edge Weight; Edge Color; Edge Type; Combining Basic Attributes; Bundles, Shapes, Images, and More; Bundled Edges; Shape; Node Image; Node Border; More Attributes; Interference and Separation; Putting It All Together; Summary; Chapter 6: Explore and Explain; Explore, Explain, and Export; Essential Exploratory Interactions; Zoom and Pan (and Scale and Rotate ...); Identify; Filter ; Isolate and Redo Layout; More Interactive Exploration.
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|a Identifying Neighbors Paths; Deleting; Grouping; Iterative Analysis; Explain; Sequence of a Data Story; Legends; Annotations; Export Data Subsets, Graphs, and Images; Putting It All Together; Summary; Chapter 7: Point-and-Click ; Excel; Summarizing Links; Extracting Nodes ; Adjacency Matrix Visualization in Excel; NodeXL; NodeXL Basics; Social Network Features; Gephi; Gephi Basics; Caveats; Cytoscape; Cytoscape Basics; Importing Data into Cytoscape; Visual Attributes; Apps Menu; yEd; yEd Basics; Summary; Chapter 8: Lightweight Programming; Python; Getting Started; Cleaning Data.
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|a Extracting a Set of Nodes from a Link Data SetTransforming E-mail Data into a Graph; Graph Databases; JavaScript and Graph Visualization; D3 Basics; D3 and Graphs; D3 Springy Graph ; Summary; Part 3: Visual Analysis ; Chapter 9: Relationships; Links and Relationships; Similarities in Fraud Claims; Cybersecurity; E-mail Relationships; Spatial Separation; Actors and Movies; Links Turned into Nodes; Summary; Chapter 10: Hierarchies; Organizational Charts; Trees and Graphs; Drawing a Hierarchy; Decision Trees; Website Trees and Effectiveness; Summary; Chapter 11: Communities.
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| 500 |
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|a What Defines a Community?
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| 520 |
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|a Wring more out of the data with a scientific approach toanalysis Graph Analysis and Visualization brings graph theory outof the lab and into the real world. Using sophisticated methods andtools that span analysis functions, this guide shows you how toexploit graph and network analytic techniques to enable thediscovery of new business insights and opportunities. Published infull color, the book describes the process of creating powerfulvisualizations using a rich and engaging set of examples fromsports, finance, marketing, security, social media, and more. Youwill find practical guidance towar.
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| 650 |
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|a Network analysis (Planning)
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| 650 |
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|a Graph theory
|x Data processing.
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| 650 |
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|a Business
|x Data processing.
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| 650 |
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|a Management
|x Data processing.
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| 650 |
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7 |
|a Management
|x Data processing
|2 fast
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| 650 |
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7 |
|a Business
|x Data processing
|2 fast
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| 650 |
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7 |
|a Graph theory
|x Data processing
|2 fast
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| 650 |
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7 |
|a Network analysis (Planning)
|2 fast
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| 700 |
1 |
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|a Jonker, David.
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| 758 |
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|i has work:
|a Graph analysis and visualization (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGy7YttJjgdTqC98gX9v73
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|a Brath, Richard.
|t Graph Analysis and Visualization : Discovering Business Opportunity in Linked Data.
|d Hoboken : Wiley, ©2015
|z 9781118845844
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1895654
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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