Index
A
- aggregateMessages operator
- about / The aggregateMessages operator
- EdgeContext parameter / EdgeContext
- EdgeContext / EdgeContext
- aggregation, abstracting / Abstracting out the aggregation
- DRY principle / Keeping things DRY
- arguments, adding / Coach wants more numbers
- average point per game, calculating / Calculating average points per game
- defense stats / Defense stats – D matters as in direction
- analysis, of network connectedness
- about / The analysis of network connectedness
- connected components, finding / Finding the connected components
- triangle, counting / Counting triangles and computing clustering coefficients
- clustering coefficients, computing / Counting triangles and computing clustering coefficients
- Apache Zeppelin
- about / The graph visualization
- average stats
- joining, into graph / Joining average stats into a graph
B
- bipartite food network example
- bipartite graph
- building / Building a bipartite graph
- BreezeViz
- about / The graph visualization
- installing / Installing the GraphStream and BreezeViz libraries
- URL, for downloading / Installing the GraphStream and BreezeViz libraries
C
- clustering
- about / Community clustering in graphs
- ColorBrewer
- communication network
- about / The communication network
- community clustering, in graphs
- about / Community clustering in graphs
- spectral clustering / Spectral clustering
- power iteration clustering (PIC) / Power iteration clustering
- community detection
- through label propagation / Community detection through label propagation
- compound nodes
- about / Flavor networks
D
- degree distribution
- plotting, of network / Plotting the degree distribution
- degree histogram, of social ego networks
- computing / Degree histogram of the social ego networks
- degrees, in bipartite food network
- computing / Degrees in the bipartite food network
- degrees of network nodes
- computing / Computing the degrees of the network nodes
- dependencies
- about / Managing library dependencies
- directed graphs
- building / Building directed graphs
E
- edge attributes
- transforming / Transforming the vertex and edge attributes
- EdgeContext parameter / EdgeContext
- edge directions
- reversing / Reversing edge directions
- edgeListFile graph builder / edgeListFile
- EdgeRDD
- data operations on / Data operations on VertexRDD and EdgeRDD
- mapping / Mapping VertexRDD and EdgeRDD
- EdgeRDDs
- joining / Joining EdgeRDDs
- email communication graph
- about / The communication network
- Enron Corpus
- about / The communication network
- URL / The communication network
F
- files and directories, Spark 1.4.1
- core / Downloading and installing Spark 1.4.1
- bin / Downloading and installing Spark 1.4.1
- graphx / Downloading and installing Spark 1.4.1
- mllib, sql / Downloading and installing Spark 1.4.1
- streaming / Downloading and installing Spark 1.4.1
- examples / Downloading and installing Spark 1.4.1
- flavor network
- about / Flavor networks
- references / Flavor networks
- fromEdges graph builder / fromEdges
- fromEdgeTuples graph builder / fromEdgeTuples
G
- Gaussian Mixture Model (GMM)
- about / Community clustering in graphs
- Gephi
- about / The graph visualization
- graph
- about / Network datasets
- average stats, joining into / Joining average stats into a graph
- graph builders
- about / Graph builders
- Graph factory method / The Graph factory method
- edgeListFile / edgeListFile
- fromEdges / fromEdges
- fromEdgeTuples / fromEdgeTuples
- graph data
- visualizing / Visualizing the graph data
- graph datasets, joining
- about / Joining graph datasets
- joinVertices operator / joinVertices
- outerJoinVertices operator / outerJoinVertices
- Graph factory method
- about / The Graph factory method
- graphs
- building / Building graphs
- GraphStream
- about / The graph visualization
- installing / Installing the GraphStream and BreezeViz libraries
- URL, for downloading / Installing the GraphStream and BreezeViz libraries
- URL / Visualizing the graph data
- graph structures, modifying
- about / Modifying graph structures
- reverse operator / The reverse operator
- subgraph operator / The subgraph operator
- mask operator / The mask operator
- groupEdges operator / The groupEdges operator
- graph visualization
- about / The graph visualization
- GraphX
- about / Getting started with GraphX
- tiny social network, building / Building a tiny social network
- standalone application, building / Building and submitting a standalone application
- standalone application, submitting / Building and submitting a standalone application
- groupEdges operator
- about / The groupEdges operator
H
- Hollywood movie graph example
- about / Example – Hollywood movie graph
I
- in-degree, of Enron email network
- ingredient nodes
- about / Flavor networks
- installation
- Spark 1.4.1 / Downloading and installing Spark 1.4.1
J
- Java Development Kit 7 (JDK) / Downloading and installing Spark 1.4.1
- Java Runtime Environment (JRE)
- URL, for download / Downloading and installing Spark 1.4.1
- Java virtual machine (JVM) / Downloading and installing Spark 1.4.1
- JfreeChart
- joinVertices operator / joinVertices
L
- Label propagation algorithm (LPA)
- Latent Dirichlet Allocation (LDA)
- about / Community clustering in graphs
M
- mapEdges
- transforming / mapEdges
- MapReduceTriplets operator
- about / The MapReduceTriplets operator
- mapTriplets
- transforming / mapTriplets
- mapVertices
- transforming / mapVertices
- mask operator
- about / The mask operator
- music fan community detection application
- about / Applications – music fan community detection
- data, loading into Spark graph property / Step 1 – load the data into a Spark graph property
- features of nodes, extracting / Step 2 – extract the features of nodes
- similarity measure, defining between two nodes / Step 3 – define a similarity measure between two nodes
- affinity matrix, creating / Step 4 – create an affinity matrix
- k-means clustering, running on affinity matrix / Step 5 – run k-means clustering on the affinity matrix
- collaborative clustering, by playlist / Exercise – collaborative clustering through playlists
N
- NCAA College Basketball datasets
- about / NCAA College Basketball datasets
- neighboring information
- collecting / Collecting neighboring information
- network centrality
- network connectedness
- analysis / The analysis of network connectedness
- network datasets
- about / Network datasets, Network datasets
- communication network / The communication network
- flavor network / Flavor networks
- social ego networks / Social ego networks
O
- out-degree, of Enron email network
- outerJoinVertices operator / outerJoinVertices
P
- PageRank
- about / The network centrality and PageRank
- working / How PageRank works
- performance optimization
- about / Performance optimization
- power iteration clustering (PIC)
- about / Community clustering in graphs, Power iteration clustering
- reference link / Power iteration clustering
- Pregel API, in GraphX
- about / The Pregel API in GraphX
- Pregel computational model
- about / The Pregel computational model
- iterating, towards social equality / Example – iterating towards the social equality
- Pregel implementation, of PageRank / The Pregel implementation of PageRank
- property graph / The property graph
R
- resolvers
- about / Managing library dependencies
- reverse operator
- about / The reverse operator
S
- .sbt build files
- reference link / Organizing build definitions
- SBT
- about / Managing library dependencies
- SBT commands
- tasks, running with / Running tasks with SBT commands
- Scala Build Tool
- about / Scala Build Tool revisited
- build definitions, organizing / Organizing build definitions
- library dependencies, managing / Managing library dependencies
- Scala Build Tool (SBT)
- about / Building the program with the Scala Build Tool
- URL / Building the program with the Scala Build Tool
- used, for building program / Building the program with the Scala Build Tool
- share circle feature
- about / Social ego networks
- social ego networks
- about / Social ego networks
- reference link / Social ego networks
- Spark
- URL, for download / Downloading and installing Spark 1.4.1
- programming guide, URL / Experimenting with the Spark shell
- application, URL / Deploying and running with spark-submit
- URL, for documentation / Finding the connected components
- spark-submit
- used, for running standalone application / Deploying and running with spark-submit
- used, for deploying standalone application / Deploying and running with spark-submit
- Spark 1.4.1
- downloading / Downloading and installing Spark 1.4.1
- installing / Downloading and installing Spark 1.4.1
- Spark application, building steps
- about / A preview of the steps
- sbt-assembly plugin, enabling / Step 1 – Enable the sbt-assembly plugin
- build.sbt file, creating / Step 2 – Create a build.sbt file
- library dependencies, declaring / Step 3 – Declare library dependencies and resolvers
- resolvers, declaring / Step 3 – Declare library dependencies and resolvers
- sbt-assembly plugin, setting up / Step 4 – Set up the sbt-assembly plugin
- uber JAR, creating / Step 5 – Create the uber JAR
- Spark program
- writing / Writing and configuring a Spark program
- configuring / Writing and configuring a Spark program
- URL / Writing and configuring a Spark program
- spark.app.name property / Writing and configuring a Spark program
- spark.executor.memory property / Writing and configuring a Spark program
- spark.driver.memory property / Writing and configuring a Spark program
- spark.storage.memoryFraction property / Writing and configuring a Spark program
- spark.serializer property / Writing and configuring a Spark program
- Spark shell
- experimenting with / Experimenting with the Spark shell
- spectral clustering
- about / Spectral clustering
- reference link / Spectral clustering
- standalone application
- submitting / Building and submitting a standalone application
- building / Building and submitting a standalone application
- Spark program, writing / Writing and configuring a Spark program
- Spark program, configuring / Writing and configuring a Spark program
- program, building with Scala Build Tool / Building the program with the Scala Build Tool
- spark-submit, downloading / Deploying and running with spark-submit
- spark-submit, running / Deploying and running with spark-submit
- subgraph operator
- about / The subgraph operator
- superstep
- supersteps
- about / The Pregel computational model
T
- tasks
- running, with SBT commands / Running tasks with SBT commands
- tiny social network
- building / Building a tiny social network
- data, loading / Loading the data
- property graph / The property graph
- RDDs, transforming to VertexRDD / Transforming RDDs to VertexRDD and EdgeRDD
- RDDs, transforming to EdgeRDD / Transforming RDDs to VertexRDD and EdgeRDD
- graph operations / Introducing graph operations
- triplet view / Introducing graph operations
V
- vertex attributes
- transforming / Transforming the vertex and edge attributes
- vertex program
- VertexRDD
- data operations on / Data operations on VertexRDD and EdgeRDD
- mapping / Mapping VertexRDD and EdgeRDD
- VertexRDDs
- filtering / Filtering VertexRDDs
- joining / Joining VertexRDDs
W
- web pages
- ranking / Ranking web pages
- weighted social ego network
- building / Building a weighted social ego network