Transcript Ranking
Ranking
Ida Mele
Introduction
• The set of software components for the management of
large sets of data is made of:
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MG4J
Fastutil
the DSI Utilities
Sux4J
WebGraph
the LAW software
• These software components have been developed by the
DSI of the University of Milan
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Fastutil
• Fastutil 6 is a free software, developed in Java
• Technical requirement:
• Java >= 6
• Useful links:
• http://fastutil.di.unimi.it/
• http://fastutil.di.unimi.it/docs/
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Fastutil
• Fastutil extends Java Collections, and it
provides:
• Type-specific maps, sets, and lists
• Priority queues with a small memory footprint and
fast access and insertion
• 64-bit arrays, sets, and lists
• Fast I/O classes for text and binary files
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Fastutil: Advantages
• Advantages in using Fastutil:
• Classes of Fastutil are implemented in order to
work on huge collections of data in an efficient
way
• Fastutil provides a new set of classes to deal with
collections whose size exceeds 231
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Fastutil: Advantages
• Advantages in using Fastutil:
• There are additional features (e.g., bidirectional
iterators) that are not available in the standard
classes
• Classes can be plugged into existing code, because
they implement their standard counterpart (e.g.,
Map is used for maps)
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Fastutil: Big Arrays
• BigArrays: This class provides static methods and
objects for working with big arrays
• Big arrays are arrays-of-arrays. For example, a big
array of integers has type int[][]
• Methods handle these arrays-of-arrays as if they are
monodimensional arrays with 64-bit indices
• The length of a big array is bounded by
Long.MAX_VALUE rather than Integer.MAX_VALUE
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Fastutil: Big Arrays
• Given a big array a, a[0], a[1], … a[n] are called segments. Each
one has length SEGMENT_SIZE (the last segment can have a
smaller size)
• Each index i is associated with a segment and a displacement
into the segment
• Methods segment/displacement compute the
segment/displacement associated with a given index
• Method index receives the segment and the displacement and
returns the corresponding index
• Methods get/set allow to return/set the value of a given
element in the big array
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Fastutil Big Arrays – example
• We want to scan the big array a
• First solution:
for( int s = 0; s < a.length; s++ ) {
final int[] t = a[ s ];
for( int d = 0; d < t.length; d++ ) {
//do something with t[ d ]
}
}
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Fastutil Big Arrays – example
• Second solution:
for( int s = a.length; s-- != 0; ) {
final int[] t = a[ s ];
for( int d = t.length; d-- != 0; ) {
//do something with t[ d ]
}
}
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Fastutil Big Arrays – example
• Third solution:
for( int s = a.length; s-- != 0; ) {
final long[] t = a[ s ];
for( int d = t.length; d-- != 0; )
t[d] = index( s, d );
}
• We can use the index method, which returns the
index associated with a segment and displacement
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Fastutil: Big Data Structures
• Fastutil provides classes also for other data
structures:
• BigList: a list with indices. The instances of this
class implement the same semantics of traditional
List
• HashBigSet: the instances of this class use a hash
table to represent a big set. The number of
elements in the set is limited only by the amount
of core memory
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Dsiutils
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The DSI utilities are a mishmash of classes
Free software
Developed in Java
Useful links:
• http://dsiutils.di.unimi.it/
• http://dsiutils.di.unimi.it/docs/
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Dsiutils: MultipleString
• In large-scale text indexing we want to use a
mutable string that, once frozen, can be used in
the same optimized way of an immutable string
• In Java we have String and StringBuffer, which
can be used for immutable and mutable strings
respectively
• The solution is MultipleString
• MultipleString does not need synchronization
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Dsiutils: Packages
• Some important packages:
• it.unimi.dsi.bits contains main classes for
manipulating bits. For example, the class BitVectors
provides static methods and objects that do useful
things with bit vectors
• it.unimi.dsi.compression provides word-based
compression/decompression classes
• it.unimi.dsi.util offers implementations of
BloomFilters, PrefixMaps, StringMaps, BinaryTries and
others
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WebGraph
• WebGraph is a framework for graph
compression
• It exploits modern compression techniques to
manage very large graphs
• Useful links:
• http://webgraph.di.unimi.it/
• http://webgraph.di.unimi.it/docs/
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WebGraph
• WebGraph provides:
• ζ-codes, which are suitable for storing web graphs
• Algorithm for compressing the graph that exploit
gap compression as well as ζ-codes. The
parameters provide different tradeoffs between
access speed and compression ratio
• Algorithms to access to compressed graphs
without decompression. The lazy techniques delay
the decompression until it is necessary
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WebGraph: Classes
• Some important classes:
• ImmutableGraph is an abstract class representing
an immutable graph
• BVGraph allows to store and access web graphs in
a compressed form
• ASCIIGraph is used to store the graph in a humanreadable ASCII format
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WebGraph: Classes
• Some important classes:
• ArcLabelledImmutableGraph is an abstract
implementation of a graph with labeled arcs
• Transform returns the transformed version of an
immutable graph. We can use the transpose
method of this class if we want to create the
transpose graph
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LAW
• Java software developed by the Laboratory for
Web Algorithms
• It is free and contains several implementations
of the Pagerank algorithm
• Useful links:
• http://law.di.unimi.it/software.php
• http://law.di.unimi.it/software/docs/index.html
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LAW: PageRank
• PageRank of the package it.unimi.dsi.law.rank is an
abstract class that defines methods and attributes
for the PageRank algorithm
• Provided features:
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we can set the preference vectors
we can set the damping factor
we can program stopping criteria
step-by-step execution
reusability
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Exercise
• Download the archive with libraries: lib.zip
• Download the files:
• set-classpath.sh
• example
• Text2ASCII.class and PrintRanks.class
available at:
http://www.dis.uniroma1.it/~mele/WebIR.html
• Set the classpath using the command:
source set-classpath.sh
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Build the Graph: Step1
• Step 1 - Create the file in the format ASCIIGraph
using the command:
java Text2ASCII example
• Output:
example.graph-txt: the first line contains the number of
nodes (e.g., n). The following n lines contain the list of
out-neighbours of the nodes. In particular, the line i-th
contains the successors of the node i, sorted in an
increasing order and separated by a space
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Build the Graph: Step1
input:
example
0
0
0
1
1
1
2
2
2
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…
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8
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4
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1
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output:
example.graph-txt
Text2ASCII
10
1 8
4 7
1 3
1 4
1
1
1
5
0
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4 5 6 7 8 9
5 6 9
2
2 3 4 5
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1 3 4 6
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Build the Graph: Step1
more example.graph-txt
Node id
.
.
.
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1 8
4 7
1 3
1 4
1
1
1
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0
Num of nodes
9
9
4 5 6 7 8 9
5 6 9
Lists of successors
2
2 3 4 5
9
1 3 4 6
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Build the Graph: Step2
• We can use the main method of the BVGraph class to
load and compress an ImmutableGraph
• The compressed graph is described by:
• basename.graph: the graph file. It contains the successor
lists, one for each node. Each list is a sequence of natural
number that are coded as sequence of bits in a efficient
way
• basename.offsets: the offset file. It stores the offset for
each node of the graph
• basename.properties: the file with properties and
statistics
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Build the Graph: Step2
• Step 2 - Conversion from the ASCIIGraph to
the BVGraph:
java it.unimi.dsi.big.webgraph.BVGraph -g
ASCIIGraph example exampleBV
• Output:
• exampleBV.graph
• exampleBV.offsets
• exampleBV.properties
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Build the Graph: Step2
more exampleBV.properties
…
compratio=1,89
bitsforblocks=22
residualarcs=15
version=0
…
nodes=10
compressionflags=
intervalisedarcs=10
bitspernode=16,8
arcs=34
…
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Compute PageRank
• To compute the PageRank we can use the following
implementations:
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PowerMethod
PageRankPowerSeries
GaussSeidel
Jacobi
• The output is made of 2 files:
• basename.ranks: binary file with the results of
computation
• basename.properties: text files with general info
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Compute PageRank: Step 1
• Step 1 - We use the main method of the class
PageRankPowerMethod by issuing the following
command:
java it.unimi.dsi.law.rank.PageRankPowerMethod
exampleBV examplePR
• Output:
• examplePR.ranks
• examplePR.properties
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Compute PageRank: Step 1
more examplePR.properties
rank.alpha = 0.85
rank.stronglyPreferential = false
method.numberOfIterations = 12
method.norm.type = INFTY
method.norm.value = 8.396275630317973E-7
graph.nodes = 10
graph.fileName = example
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Compute PageRank: Step 2
• Step 2 – Print the scores
• The file .ranks is a binary file with the scores of
the nodes, so we can print PageRank scores by
using the class PrintRanks:
java PrintRanks examplePR.ranks > ranks
• Output:
ranks: file with n lines, one for each node. The i-th
line contains the score of node number i
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Compute PageRank: Step 2
more ranks
Node id
.
.
.
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0.0515659940361598
0.20197850631669495
0.07982657817906964
0.07587785830476211
0.14600457683651308
0.08608501191896127
0.07294688611466064
0.0931194920828582
0.05050241152172527
0.14209268468859523
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PageRank values
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Compute PageRank: sorting
sort –r ranks
0.20197850631669495
0.14600457683651308
0.14209268468859523
0.0931194920828582
0.08608501191896127
0.07982657817906964
0.07587785830476211
0.07294688611466064
0.0515659940361598
0.05050241152172527
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PageRank values
sorted in
decreasing order
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Homework
1. Repeat the exercise with the graphs:
• WikiIT
• WikiPT
available at:
http://www.dis.uniroma1.it/~mele/WebIR.html
2. Create a new graph by using synthetic or real data,
and repeat the exercise
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