Download Waikato Machine Learning Group Talk on Graph-RAT

Graph-RAT Overview
By
Daniel McEnnis
What is Graph-RAT
 Relational Analysis Toolkit
 Database abstraction layer
 Evaluation platform
 Robustly evaluate all different ways of
performing recommendation
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Kinds of Analysis
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Recommendation Systems
Data Mining
Relational Machine Learning
MIR document retrieval
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Talk Outline
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Base Components
Queries
Algorithms
Schedulers
Graph-RAT Language
Conclusion and Examples
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Base Components
 Graphs
 Actors
 Links
 Properties
Name
John
E
Library
[Vector]
A
A
Age
B
Hobbies
C Hiking
Biking
22
D
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Properties
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Variables of Graph-RAT
Can be arbitrary Java types
Can be attached to anything
Unique ID string for each object
Accessed only as sets, not as objects
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Data View
 Hyper-graph structure defined by the set of
actors and links in a graph
 Accessible from the enclosing graph
 Can be cyclic
A
B
E
C
D
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Metadata View
 Not constructed by default
 Implicit graph described by modes and the
relations between them
 Needed for relational machine learning
User
Friend
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Query Language
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Constructs sets retrieved from a graph
Functional structure
Similar to SQL
4 types
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Graph Queries
Actor Queries
Link Queries
Property Queries
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Query Structure
 Cascading queries in a LISP style syntax
 Each child query is of a different type
 Restrictions can be added at runtime
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Query Examples
 LinkByActor(
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false,
ActorByMode(false, “Target”,”.*”)
ActorByMode(false, “Source”,”.*”)
SetOperation.XOR)
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Query Comparisons
 Similar to the JENA interface
 Construction is similar to Jung system
 Implements all SQL queries that do not
require temporary tables
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0.4.3 Query
 Uses graph primitives instead of Queries
 Algorithms use hard-coded GraphByID
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Algorithms
 Functions that execute over a given graph
 Metadata is a part of the algorithm
 Excepting output algorithms, no side effects
are permitted.
execute(Graph graph)
 Properties utilized or created are declared
up front.
IODescriptor getInput()
IODescriptor getOuput()
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Propositional Algorithms
 Utilizes aggregator function as a parameter
 Crosses all ways of shifting data
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Aggregate By Link
Aggregate By Link Property
Aggregate On Graph
Graph To Actor
Link To Graph
Graph To Graph
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Aggregator Functions
 1 or more elements to equal or fewer
elements
 Examples
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Statistical Moments
Arithmetic Operations
Null Aggregation
Concatentation
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Social Network Analysis
Algorithms
 Prestige Algorithms
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Degree
Betweeness
Closeness
Page Rank
HITS
 Graph Triples
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Classification Algorithms
 Machine Learning Primitives
 Uses Weka
 Separate algorithms for training and
classifying
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Clustering Algorithms
 Several graph-based algorithms
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Weak Component Clustering
Strong Component Clustering
Edge Betweeness Clustering
Norman-Girvan Edge Betweeness
 Also has primitives calling Weka on vector
data
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Similarity Algorithms
 Comparisons between modes
 Types of Similarity
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Similarity By Link
Similarity By Property
Graph Similarity
 Distance Functions
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All Weka distance functions
KLDistance
Exponential Distance
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Collaborative Filtering
Algorithms
 Traditional recommendation algorithms
 Item to Item
 User to User
 Associative Mining
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Array-Based Algorithms
 Transform To Array
 Principal Component Analysis
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Evaluation
 All forms of evaluating results
 Set Based (precision and recall)
 Weighted Set (Correlations)
 Ordered Lists (Kendall Tau, Half Life)
 Cross-Validation algorithms
 By Actor
 By Link
 By Graph
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Data Acquisition
 Components for acquiring source data
 File Reader Types
 Reading different file formats
 Web Crawling Types
 LiveJournal or LastFM
 Connection Types
 Links different sets together
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Web Crawler
 Custom Multi-threaded web crawler
 Dynamic parsers
 Properties passing between both crawls and
parser execution
 Stop and filter conditions are parameterized
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Existing Parsers
 Base HTML parsing
 XML Parsing (SAX)
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LiveJournal FOAF
LastFM REST services
Graph-RAT documents
Yahoo search queries
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Comparisons
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SQL
LINQ
Matlab
Other graph packages
Prolog?
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Embedded Use
 Dynamic Loading
 AbstractFactory abstract superclass
 Example - Retrieving links to YouTube
videos from GData
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Graph-RAT Language
 Base Graph-RAT:
 Data Acquisition components executed
 For each algorithm entry:
 Graph Query selects a set of graphs
 Algorithm is executed over each graph
 Cross-Validation Graph-RAT
 Mode, relation, or graph chosen in advance,
 Data Acquisition components run once
 Algorithm entries rerun for each fold
 Statistical Graph-RAT
 List of cross-validation schedulers
 Statistical metrics of which performed better
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User To User Collaborative
Filtering Example
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Aggregate By Link(Artist->User)
Similarity By Link (User->User)
Aggregate By Link (User->User)
Property to Link (User->Artist)
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Setup Example
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Setup Example
<Scheduler class=“BasicScheduler”>
<Graph>
<MemGraph/>
</Graph>
…
</Scheduler>
DataAquisition
<DataAcquisition>
<Class>Crawl LastFM</Class>
<Name>Crawl LastFM</Name>
<MemGraph/>
<Property><Name>Proxy</Name>
<Value>proxy.waikato.ac.nz</Value>
</Property>
…
</DataAquisition>
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Query Entry
<Algorithm>
<Query>
<GraphByID>
<Pattern>.*</Pattern>
</GraphByID>
</Query>
</Algorithm>
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Algorithm Entry
<Algorithm>
<Query>…</Query>
<Class>GraphTriples</Class>
<Name>Graph Triples</Name>
<Property><Name>Relation</Name>
<Value>Friends</Value>
</Property>
<Property><Name>Destination</Name>
<Value>TriplesVector</Value>
</Property>
…
</Algorithm>
Future Work
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Stabilization - 0.5.1 to beta
Statistical testing on result sets
Upgrading the GUI interface
Memory performance upgrades
Octave Integration
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Questions?
 http://graph-rat.sourceforge.net
 Stable (beta) release is 0.4.3
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