Search::ContextGraph - spreading activation search engine


Search-ContextGraph documentation Contained in the Search-ContextGraph distribution.

Index


Code Index:

NAME

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Search::ContextGraph - spreading activation search engine

SYNOPSIS

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  use Search::ContextGraph;

  my $cg = Search::ContextGraph->new();

  # first you add some documents, perhaps all at once...

  my %docs = (
    'first'  => [ 'elephant', 'snake' ],
    'second' => [ 'camel', 'pony' ],
    'third'  => { 'snake' => 2, 'constrictor' => 1 },
  );

  $cg->bulk_add( %docs );

  # or in a loop...

  foreach my $title ( keys %docs ) {
  	 $cg->add( $title, $docs{$title} );
  }

  #	or from a file...

  my $cg = Search::ContextGraph->load_from_dir( "./myfiles" );

  # you can store a graph object for later use

  $cg->store( "stored.cng" );

  # and retrieve it later...

  my $cg = ContextGraph->retrieve( "stored.cng" );

  


  # SEARCHING

  # the easiest way 

  my @ranked_docs = $cg->simple_search( 'peanuts' );




  # get back both related terms and docs for more power

  my ( $docs, $words ) = $cg->search('snake');




  # you can use a document as your query

  my ( $docs, $words ) = $cg->find_similar('First Document');




  # Or you can query on a combination of things

  my ( $docs, $words ) = 
    $cg->mixed_search( { docs  => [ 'First Document' ],
                         terms => [ 'snake', 'pony' ]
                     );




  # Print out result set of returned documents
  foreach my $k ( sort { $docs->{$b} <=> $docs->{$a} }
      keys %{ $docs } ) {
      print "Document $k had relevance ", $docs->{$k}, "\n";
  }







  # Reload it
  my $new = Search::ContextGraph->retrieve( "filename" );







DESCRIPTION

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Spreading activation is a neat technique for building search engines that return accurate results for a query even when there is no exact keyword match. The engine works by building a data structure called a context graph, which is a giant network of document and term nodes. All document nodes are connected to the terms that occur in that document; similarly, every term node is connected to all of the document nodes that term occurs in. We search the graph by starting at a query node and distributing a set amount of energy to its neighbor nodes. Then we recurse, diminishing the energy at each stage, until this spreading energy falls below a given threshold. Each node keeps track of accumulated energy, and this serves as our measure of relevance.

This means that documents that have many words in common will appear similar to the search engine. Likewise, words that occur together in many documents will be perceived as semantically related. Especially with larger, coherent document collections, the search engine can be quite effective at recognizing synonyms and finding useful relationships between documents. You can read a full description of the algorithm at http://www.nitle.org/papers/Contextual_Network_Graphs.pdf.

The search engine gives expanded recall (relevant results even when there is no keyword match) without incurring the kind of computational and patent issues posed by latent semantic indexing (LSI). The technique used here was originally described in a 1981 dissertation by Scott Preece.

CONSTRUCTORS

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new %PARAMS

Object constructor. Possible parameters:

auto_reweight

Rebalance the graph every time a change occurs. Default is true. Disable and do by hand using reweight_graph for better performance in graphs with frequent updates/additions/deletions.

debug LEVEL

Set this to 1 or 2 to turn on verbose debugging output

max_depth

Set the maximum distance to spread energy out from the start node. Default is effectively unlimited. You can tweak it using set_max_depth. Comes in handy if you find searches are too slow.

xs

When true, tells the module to use compiled C internals. This reduces memory requirements by about 60%, but actually runs a little slower than the pure Perl version. Don't bother to turn it on unless you have a huge graph. Default is pure Perl.

* using the compiled version makes it impossible to store the graph to disk.
* xs is broken in version 0.09. But it will return in triumph!

START_ENERGY

Initial energy to assign to a query node. Default is 100.

ACTIVATE_THRESHOLD

Minimal energy needed to propagate search along the graph. Default is 1.

COLLECT_THRESHOLD

Minimal energy needed for a node to enter the result set. Default is 1.

load_from_dir DIR [, \&PARSE ]

Load documents from a directory. Takes two arguments, a directory path and an optional parsing subroutine. If the parsing subroutine is passed an argument, it will use it to extract term tokens from the file. By default, the file is split on whitespace and stripped of numbers and punctuation.

load_from_tdm FILENAME

Opens and loads a term-document matrix (TDM) file to initialize the graph. The TDM encodes information about term-to-document links. This is a legacy method mainly for the convenience of the module author. For notes on the proper file format, see the README file. =cut

rename OLD, NEW

Renames a document. Will return undef if the new name is already in use.

retrieve FILENAME

Loads a previously stored graph from disk, using Storable.

ACCESSORS

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[get|set]_activate_threshold

Accessor for node activation threshold value. This value determines how far energy can spread in the graph. Lower it to increase the number of results. Default is 1.

[get|set]_auto_reweight

Accessor for auto reweight flag. If true, edge weights will be recalculated every time a document is added, updated or removed. This can significantly slow down large graphs. On by default.

[get|set]_collect_threshold

Accessor for collection threshold value. This determines how much energy a node must have to make it into the result set. Lower it to increase the number of results. Default is 1.

[get|set]_debug_mode LEVEL

Turns debugging on or off. 1 is verbose, 2 is very verbose, 0 is off.

[get|set]_initial_energy

Accessor for initial energy value at the query node. This controls how much energy gets poured into the graph at the start of the search. Increase this value to get more results from your queries.

[get|set]_max_depth LEVEL

You can tell the graph to cut off searches after a certain distance from the query node. This can speed up searches on very large graphs, and has little adverse effect, especially if you are interested in just the first few search results. Set this value to undef to restore the default (10^8).

METHODS

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add DOC, WORDS

Add a document to the search engine. Takes as arguments a unique doc identifier and a reference to an array or hash of words in the document. For example:

	TITLE => { WORD1 => COUNT1, WORD2 => COUNT2 ... }

or

	TITLE => [ WORD1, WORD2, WORD3 ]

Use bulk_add if you want to pass in a bunch of docs all at once.

add_file PATH [, name => NAME, parse => CODE]

Adds a document from a file. By default, uses the PATH provided as the document identifier, and parses the file by splitting on whitespace. If a fancier title, or more elegant parsing behavior is desired, pass in named arguments as indicated. NAME can be any string, CODE should be a reference to a subroutine that takes one argument (the contents of the file) and returns an array of tokens, or a hash in the form TOKEN => COUNT, or a reference to the same.

bulk_add DOCS

Add documents to the graph in bulk. Takes as an argument a hash whose keys are document identifiers, and values are references to hashes in the form { WORD1 => COUNT, WORD2 => COUNT...} This method is faster than adding in documents one by one if you have auto_rebalance turned on.

degree NODE

Given a raw node, returns the degree (raw node means the node must be prefixed with 'D:' or 'T:' depending on type )

delete DOC

Remove a document from the graph. Takes a document identifier as an argument. Returns 1 if successful, undef otherwise.

has_doc DOC

Returns true if the document with identifier DOC is in the collection

has_term TERM

Returns true if the term TERM is in the collection

distance NODE1, NODE2, TYPE

Calculates the distance between two nodes of the same type (D or T) using the formula:

	distance = ...
=cut




distance_matrix TYPE LIMIT

Used for clustering using linear local embedding. Produces a similarity matrix in a format I'm too tired to document right now. LIMIT is the maximum number of neighbors to keep for each node.

intersection @NODES

Returns a list of neighbor nodes that all the given nodes share in common

raw_search @NODES

Given a list of nodes, returns a hash of nearest nodes with relevance values, in the format NODE => RELEVANCE, for all nodes above the threshold value. (You probably want one of search, find_similar, or mixed_search instead).

reweight_graph

Iterates through the graph, calculating edge weights and normalizing around nodes. This method is automatically called every time a document is added, removed, or updated, unless you turn the option off with auto_reweight(0). When adding a lot of docs, this can be time consuming, so either set auto_reweight to off or use the bulk_add method to add lots of docs at once

update ID, WORDS

Given a document identifier and a word list, updates the information for that document in the graph. Returns the number of changes made

doc_count [TERM]

Returns a count of all documents that TERM occurs in. If no argument is provided, returns a document count for the entire collection.

doc_list [TERM]

Returns a sorted list of document identifiers that contain TERM, in ASCII-betical order. If no argument is given, returns a sorted document list for the whole collection.

dump_node NODE

Lists all of the neighbors of a node, together with edge weights connecting to them

dump_tdm [FILE]

Dumps internal state in term-document matrix (TDM) format, which looks like this:

	A B C B C B C
	A B C B C B C
	A B C B C B C

Where each row represents a document, A is the number of terms in the document, B is the term node and C is the edge weight between the doc node and B. Mostly used as a legacy format by the module author. Doc and term nodes are printed in ASCII-betical sorted order, zero-based indexing. Up to you to keep track of the ID => title mappings, neener-neener! Use doc_list and term_list to get an equivalently sorted list

near_neighbors [NODE]

Returns a list of neighbor nodes of the same type (doc/doc, or term/term) two hops away.

term_count [DOC]

Returns the number of unique terms in a document or, if no document is specified, in the entire collection.

term_list [DOC]

Returns a sorted list of unique terms appearing in the document with identifier DOC, in ASCII-betical order. If no argument is given, returns a sorted term list for the whole collection.

word_count [TERM]

Returns the total occurence count for a term, or if no argument is given, a word count for the entire collection. The word count is always greater than or equal to the term count.

search @QUERY

Searches the graph for all of the words in @QUERY. Use find_similar if you want to do a document similarity instead, or mixed_search if you want to search on any combination of words and documents. Returns a pair of hashrefs: the first a reference to a hash of docs and relevance values, the second to a hash of words and relevance values.

simple_search QUERY

This is the DWIM method - takes a query string as its argument, and returns an array of documents, sorted by relevance.

find_by_title @TITLES

Given a list of patterns, searches for documents with matching titles

find_similar @DOCS

Given an array of document identifiers, performs a similarity search and returns a pair of hashrefs. First hashref is to a hash of docs and relevance values, second is to a hash of words and relevance values.

merge TYPE, GOOD, @BAD

Combine all the nodes in @BAD into the node with identifier GOOD. First argument must be one of 'T' or 'D' to indicate term or document nodes. Used to combine synonyms in the graph.

mixed_search @DOCS

Given a hashref in the form: { docs => [ 'Title 1', 'Title 2' ], terms => ['buffalo', 'fox' ], } } Runs a combined search on the terms and documents provided, and returns a pair of hashrefs. The first hashref is to a hash of docs and relevance values, second is to a hash of words and relevance values.

store FILENAME

Stores the object to a file for later use. Not compatible (yet) with compiled XS version, which will give a fatal error.

have_edge RAWNODE1, RAWNODE2

Returns true if the nodes share an edge. Node names must be prefixed with 'D' or 'T' as appropriate.

connected_components

Returns an array of connected components in the graph. Each component is a list of nodes that are mutually accessible by traveling along edges.

BUGS

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* Can't store graph if using compiled C internals

AUTHOR

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Maciej Ceglowski <maciej@ceglowski.com>

The spreading activation technique used here was originally discussed in a 1981 dissertation by Scott Preece, at the University of Illinois.

XS implementation thanks to Schuyler Erle.

CONTRIBUTORS

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	Schuyler Erle
	Ken Williams
	Leon Brocard  

COPYRIGHT AND LICENSE

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Search-ContextGraph documentation Contained in the Search-ContextGraph distribution.
package Search::ContextGraph;

use strict;
use warnings;
use Carp;
use base "Storable";
use File::Find;
use IO::Socket;

our $VERSION = '0.15';


my $count = 0;



sub new {
	my ( $class, %params) = @_;

	# backwards compatible...
	*add_document = \&add;
	*add_documents = \&bulk_add;
	
	# plucene friendly
	*optimize	= \&reweight_graph;
	*is_indexed = \&has_doc;
	
	# fail on all unknown paramters (helps fight typos)
	my @allowed = qw/debug auto_reweight use_global_weights max_depth START_ENERGY ACTIVATE_THRESHOLD COLLECT_THRESHOLD use_file xs/;
	my %check;
	$check{$_}++ foreach @allowed;
	
	my @forbidden;
	foreach my $k ( keys %params ) {
		push @forbidden, $k unless exists $check{$k};
	}
	if ( @forbidden ) {
		croak "The following unrecognized parameters were detected: ", 
		join ", ", @forbidden;
	}


	my $obj = bless 
		{ debug => 0,
		  auto_reweight => 1,
		  use_global_weights => 1,
		  max_depth => 100000000,
		  START_ENERGY => 100,
		  ACTIVATE_THRESHOLD => 1,
		  COLLECT_THRESHOLD => .2,
	      %params,

	      depth => 0,
	      neighbors => {}, 

		}, 
	$class;
	
	
	if ( $obj->{use_file} ) {
		my %neighbors;
		use MLDBM qw/DB_File Storable/;
		use Fcntl;
		warn "Using MLDBM: $obj->{use_file}";
		$obj->{neighbors} = tie %neighbors, 'MLDBM', $obj->{use_file} or die $!;
		#$obj->{neighbors} = \%neighbors;
		
	
	}

	return $obj;

}


{
	my $parse_sub;

	sub load_from_dir  {
		my ( $class, $dir, $code ) = @_;

		croak "$dir is not a directory" unless -d $dir;

		require File::Find;
		unless ( defined $code 
				 and ref $code 
				 and ref $code eq 'CODE' ) {
			$code = sub {
				my $text = shift;
				$text =~ s/[^\w]/ /gs;
				my @toks = split /\s+/m, $text;
				return grep { length($_) > 1 } @toks;
			};
		}

		$parse_sub = $code;
		my %docs;

		# Recursively open every file and provide the contents
		# to whatever parsing subroutine we're using

		my $reader = 

			sub {
				my ( $parse ) = @_;
				return if /^\./;
				return unless -f $_;
				open my $fh, $_ or 
				   croak "Could not open file $File::Find::name: $!";
				local $/;
				my $contents = <$fh>;
				close $fh or croak "failed to close filehandle";
				my @words = $parse_sub->($contents);
				$docs{ $File::Find::name } = \@words;
			};


		find( $reader , $dir );
		my $self = __PACKAGE__->new();
		$self->bulk_add( %docs );
		return $self;
	}
}



sub load_from_tdm {
	my  ( $self, $file ) = @_;
	croak "TDM file $file does not exist" unless -f $file;
	return if $self->{'loaded'};
	$self->_read_tdm( $file );
	$self->{'loaded'} = 1;
	$self->reweight_graph();
}


sub rename {

	my ( $self, $old, $new ) = @_;
	croak "rename method needs two arguments" unless
		defined $old and defined $new;
	croak "document $old not found" unless
		exists $self->{neighbors}{ _nodeify('D', $old ) };
	
	my $bad = _nodeify( 'D', $old );
	my $good = _nodeify( 'D', $new );
	
	return if exists $self->{neighbors}{$good};
	
	my $s = $self->{neighbors};
	foreach my $n ( keys %{ $s->{$bad} } ) {
		$s->{$good}{$n} = 
		$s->{$n}{$good} =
		$s->{$bad}{$n};
		delete $s->{$bad}{$n};
		delete $s->{$n}{$bad};
	}
	delete $s->{$bad};
	return 1;

}



sub retrieve {
	my ( $self, $file ) = @_;
	croak "Must provide a filename to retrieve graph"
		unless  $file;
	croak "'$file' is not a file" unless
		-f $file;

	Storable::retrieve( $file );
}


sub get_activate_threshold {    $_[0]->{'ACTIVATE_THRESHOLD'} }
sub set_activate_threshold {
	my ( $self, $threshold ) =  @_;
	croak "Can't set activate threshold to zero"
		unless $threshold;
	croak "Can't set activate threshold to negative value"
		unless $threshold > 0;
	$self->{'ACTIVATE_THRESHOLD'} = $_[1]; 
}


sub get_auto_reweight{ $_[0]->{auto_reweight} }
sub set_auto_reweight{ $_[0]->{auto_reweight} = $_[0]->[1]; }


sub get_collect_threshold {  
	return ( $_[0]->{'xs'} ? 
		$_[0]->{Graph}->collectionThreshold :
		$_[0]->{'COLLECT_THRESHOLD'})
 }

sub set_collect_threshold {	
	 my ( $self, $newval ) = @_;

	 $newval ||=0;

	 $self->{Graph}->collectionThreshold( $newval )
	 	if $self->{'xs'};

	 $self->{'COLLECT_THRESHOLD'} = $newval || 0;
	 return 1;
}

sub get_debug_mode { $_[0]->{debug} }
sub set_debug_mode {
	my ( $self, $mode ) = @_;
	$self->{'debug'} = $mode;
}



sub get_initial_energy { $_[0]->{'START_ENERGY'} }
sub set_initial_energy { 
	my ( $self, $start_energy ) = @_;
	croak "Can't set initial energy to zero"
		unless $start_energy;
	croak "Can't set initial energy to negative value"
		unless $start_energy > 0;
	$self->{'START_ENERGY'} = $start_energy ;
}

sub get_max_depth { $_[0]->{max_depth} }
sub set_max_depth { croak "Tried to set maximum depth to an undefined value" 
	 unless defined $_[1];
	 $_[0]->{max_depth} = $_[1] || 100000000 
}





sub add {

	my ( $self, $title, $words ) = @_;


	croak "Please provide a word list" unless defined $words;
	croak "Word list is not a reference to an array or hash"
		unless ref $words and ref $words eq "HASH" or ref $words eq "ARRAY";

	croak "Please provide a document identifier" unless defined $title;

	my $dnode =  _nodeify( 'D', $title );
	croak "Tried to add document with duplicate identifier: '$title'\n"
		if exists $self->{neighbors}{$dnode};

	my @list;
	if ( ref $words eq 'ARRAY' ) {
		@list = @{$words};
	} else {
		@list = keys %{$words};
	}

	croak "Tried to add a document with no content" unless scalar @list;

	my @edges;
	foreach my $term ( @list ) {
		my $tnode = _nodeify( 'T', lc( $term ) );

		# Local weight for the document
		my $lcount = ( ref $words eq 'HASH' ? $words->{$term} : 1 );

		# Update number of docs this word occurs in
		my $gcount = ++$self->{term_count}{lc( $term )};

		my $final_weight = 1;
		push @edges, [ $dnode, $tnode, $final_weight, $lcount ];

	}
	$self->{reweight_flag} = 1;
	__normalize( \@edges );


		
	# PURE PERL VERSION 
	#}  else 	{
		foreach my $e ( @edges ) {
			$self->{neighbors}{$e->[0]}{$e->[1]} = join ',', $e->[2], $e->[3];
			$self->{neighbors}{$e->[1]}{$e->[0]} = join ',', $e->[2], $e->[3];
		}
	#}
	
	
	#print "Reweighting graph\n";
	$self->reweight_graph() if $self->{auto_reweight};
	return 1;

}


sub add_file {
	my ( $self, $path, %params ) = @_;
	
	croak "Invalid file '$path' provided to add_file method."
		unless defined $path and -f $path;
		
	my $title = ( exists $params{name} ? $params{name} : $path );

	local $/;
	open my $fh, $path or croak "Unable to open $path: $!";
	my $content = <$fh>;
	
	my $ref;
	
	if ( exists $params{parse} ) {
		croak "code provided is not a reference" unless
			ref $params{parse};
		croak "code provided is not a subroutine" unless
			ref $params{parse} eq 'CODE';
		
		$ref = $params{parse}->( $content );
		croak "did not get an appropriate reference back after parsing"
			unless ref $ref and ref $ref =~ /(HASH|ARRAY)/;
		
		
	} else {
	
		my $code = sub { 
			my $txt  = shift; 
			$txt =~ s/\W/ /g;
			my @toks = split m/\s+/, $txt;
			\@toks;
		};
		$ref = $code->($content);
	}
	
	return unless $ref;
	$self->add( $title, $ref );
	
}

sub bulk_add {

	my ( $self, %incoming_docs ) = @_;

	# Disable graph rebalancing until we've added everything
	{
		local $self->{auto_reweight} = 0;

		foreach my $doc ( keys %incoming_docs ) {
			$self->add( $doc, $incoming_docs{$doc});
		} 
	}
	$self->reweight_graph() if $self->{auto_reweight};
}


sub degree { scalar keys %{$_[0]->{neighbors}{$_[1]}} }


sub delete {

	my ( $self, $type, $name ) = @_;
	
	croak "Must provide a node type to delete() method" unless defined $type;
	croak "Invalid type $type passed to delete method.  Must be one of [TD]"
		unless $type =~ /^[TD]$/io;
	croak "Please provide a node name" unless defined $name;
	
	return unless defined $name;
	my $node = _nodeify( $type, $name);

	my $n = $self->{neighbors};
	croak "Found a neighborless node $node"
		unless exists $n->{$node};

	my @terms = keys %{ $n->{$node} };

	warn "found ", scalar @terms, " neighbors attached to $node\n"
		if $self->{debug};
	# Check to see if we have orphaned any terms
	foreach my $t ( @terms ) {
		
		delete $n->{$node}{$t};
		delete $n->{$t}{$node};

		if ( scalar keys %{ $n->{$t} } == 0 ) {
			warn "\tdeleting orphaned node $t" if $self->{debug};
			my ( $subtype, $name ) = $t =~ /^(.):(.*)$/;
			#$self->delete( $subtype, $name );
			delete $n->{$t};
		}
	}

	delete $n->{$node};
	$self->check_consistency();
	$self->{reweight_flag} = 1;
	$self->reweight_graph if $self->{auto_reweight};
	1;
}



sub has_doc { 
	my ( $self, $doc ) = @_;
	carp "Received undefined value for has_doc" unless defined $doc;
	my $node = _nodeify( 'D', $doc );
	return exists $self->{neighbors}{$node} ||  undef;
}

sub has_term { 
	my ( $self, $term ) = @_;
	carp "Received undefined value for has_term" unless defined $term;
	my $node = _nodeify( 'T', $term );
	return exists $self->{neighbors}{$node} || undef;
}	



sub distance {
	my ( $self, $n1, $n2, $type ) = @_;
	croak unless $type;
	$type = lc( $type );
	croak unless $type =~ /^[dt]$/;
	my $key = ( $type eq 't' ? 'terms' : 'documents' );
	my @shared = $self->intersection( $key => [ $n1, $n2 ] );
	return 0 unless @shared;
	#warn "Found ", scalar @shared, " nodes shared between $n1 and $n2\n";
	
	my $node1 = _nodeify( $type, $n1 );
	my $node2 = _nodeify( $type, $n2 );
	# formula is w(t1,d1)/deg(d1) + w(t1,d2)/deg(d2) ... ) /deg( t1 )
	
	#warn "Calculating distance\n";
	my $sum1 = 0;
	my $sum2 = 0;
	foreach my $next ( @shared ) {
		my ( undef, $lcount1) =  split m/,/, $self->{neighbors}{$node1}{$next};
		my ( undef, $lcount2) =  split m/,/, $self->{neighbors}{$node2}{$next};

		my $degree = $self->degree( $next );
		#warn "\t degree of $next is $degree\n";
		my $elem1 = $lcount1 / $degree;
		$sum1 += $elem1;
		my $elem2 = $lcount2 / $degree;
		$sum2 += $elem2;
	}
	#warn "sum is $sum1, $sum2\n";
	my $final = ($sum1 / $self->degree( $node1 )) + ( $sum2 / $self->degree( $node2 ));
	#warn "final is $final\n";
	return $final;
	
	
}

sub distance_matrix {
	my ( $self, $type, $limit ) = @_;
	croak "Must provide type argument to distance_matrix()" 
		unless defined $type;
	croak "must provide limit" unless $limit;
	my @nodes;
	if ( lc( $type ) eq 'd' ) {
		@nodes = $self->doc_list();
	} elsif ( lc( $type ) eq 't' ) {
		@nodes = $self->term_list();
	} else {
		croak "Unsupported type $type";
	}
	
	my @ret;
	my $count = 0;
	foreach my $from ( @nodes ) {
		warn $from, " - $count\n";
		$count++;
		my $index = -1;
		my @found;
		foreach my $to ( @nodes ) {
			$index++;
			next if $from eq $to;
			my $dist = $self->distance( $from, $to, $type );
			push @found, [ $index, $dist ] if $dist;
			#print( $index++, ' ', $dist, " " ) if $dist;
		}
		my @sorted = sort { $b->[1] <=> $a->[1] } @found;
		my @final = splice ( @sorted, 0, $limit );
		push @ret, join " ", ( map { join ' ', $_->[0],  substr($_->[1], 0, 7)  } 
						  sort { $a->[0] <=> $b->[0] } 
						  @final), "\n";
		#print "\n";
	}
	return join "\n", @ret;

}

sub intersection {
	my ( $self, %nodes ) = @_;
	my @nodes;
	if ( exists $nodes{documents} ) {
		push @nodes, map { _nodeify( 'D', $_ ) } @{ $nodes{documents}};
	} 
	if ( exists $nodes{terms} ) {
		push @nodes, map { _nodeify( 'T', $_ ) } @{ $nodes{terms}};
	} 
	
	my %seen;
	foreach my $n ( @nodes ) {
		my @neighbors = $self->_neighbors( $n );
		$seen{ $_ }++ foreach @neighbors;
	}
	return map { s/^[DT]://; $_ }
		   grep { $seen{$_} == scalar @nodes } 
		   keys %seen;
}

sub raw_search {
	my ( $self, @query ) = @_;

	$self->_clear();
	foreach ( @query ) {
		$self->_energize( $_, $self->{'START_ENERGY'});
	}
	my $results_ref = $self->_collect();


	return $results_ref;
}




sub reweight_graph {
	my ( $self ) = @_;

	my $n = $self->{neighbors}; #shortcut
	my $doc_count = $self->doc_count();
	#print "Renormalizing for doc count $doc_count\n" if $self->{debug};
	foreach my $node ( keys %{$n} ) {

		next unless $node =~ /^D:/o;
		warn "reweighting at node  $node\n" if $self->{debug} > 1;
		my @terms = keys %{ $n->{$node} };
		my @edges;
		foreach my $t ( @terms ) {

			my $pair = $n->{$node}{$t};
			my ( undef, $lcount ) = split /,/, $pair;
			( my $term = $t ) =~ s/^T://;
			croak "did not receive a local count" unless $lcount;
			my $weight;
			if ( $self->{use_global_weights} ) {

				my $gweight = log( $doc_count / $self->doc_count( $term ) ) + 1;
				my $lweight = log( $lcount ) + 1;
				$weight = ( $gweight * $lweight );
				
			} else {

				$weight = log( $lcount ) + 1;
			}
			push @edges, [ $node, $t, $weight, $lcount ];
		}

		__normalize( \@edges );

		foreach my $e ( @edges ) {
			my $pair = join ',', $e->[2], $e->[3];
			$n->{$node}{$e->[1]} = $n->{$e->[1]}{$node} = $pair;
		}
	}
	$self->{reweight_flag} = 0;
	return 1;
}




sub update {

	my ( $self, $id, $words ) = @_;

	croak "update not implemented in XS" if $self->{xs};
	croak "Must provide a document identifier to update_document" unless defined $id;
	my $dnode = _nodeify( 'D', $id );

	return unless exists $self->{neighbors}{$dnode};
	croak "must provide a word list " 
		unless defined $words and 
						ref $words and
					  ( ref $words eq 'HASH' or
						ref $words eq 'ARRAY' );

	my $n = $self->{neighbors}{$dnode};
	
	# Get the current word list
	my @terms = keys %{ $n };

	if ( ref $words eq 'ARRAY' ) {
		my %words;
		$words{$_}++ foreach @$words;
		$words = \%words;
	}

	local $self->{auto_reweight} = 0;

	my $must_reweight = 0;
	my %seen;

	foreach my $term ( keys %{$words} ) {

		my $t = _nodeify( 'T', $term );

		if ( exists $n->{$t} ){

			# Update the local count, if necessary
			my $curr_val = $n->{$t};
			my ( undef, $loc ) = split m/,/, $curr_val;

			unless ( $loc == $words->{$term} ) {
				$n->{$t} = join ',', 1, $words->{$term};
				$must_reweight++;
			}	
			}

		else {

			$n->{$t} = 
				$self->{neighbors}{$t}{$dnode} = 
				join ',', 1, $words->{$term};
			$must_reweight++;
		}

		$seen{$t}++;
	}

	# Check for deleted words
	foreach my $t ( @terms ) {
		$must_reweight++ 
			unless exists $seen{$t};
	}

	$self->reweight_graph() if 
		$must_reweight;

	return $must_reweight;

}


sub doc_count {
	my ( $self, $term ) = @_;
	if ( defined $term ) {
		$term = _nodeify( 'T', $term ) unless $term =~ /^T:/;
		my $node = $self->{neighbors}{$term};
		return 0 unless defined $node;
		return scalar keys %{$node};
	} else {
		return scalar grep /D:/, 
			keys %{ $self->{'neighbors'} };
	}
}


sub doc_list {
	my ( $self, $term ) = @_;
	my $t;
	if ( defined $term and $term !~ /T:/) {
		$t = _nodeify( 'T', $term );
	}
	my $hash = ( defined $term ?
				 $self->{neighbors}{$t} :
				 $self->{neighbors} );

	sort map { s/^D://o; $_ }
		 grep /^D:/, keys %{ $hash };
}


sub dump {
	my ( $self ) = @_;
	my @docs = $self->doc_list();

	foreach my $d ( @docs ) {
		print $self->dump_node( $d );
	}
}

sub dump_node {
	my ( $self, $node ) = @_;

	my @lines;
	push @lines, join "\t", "COUNT", "WEIGHT", "NEIGHBOR";

	foreach my $n ( keys %{ $self->{neighbors}{$node} } ) {
		my $v = $self->{neighbors}{$node}{$n};
		my ( $weight, $count ) = split /,/, $v;
		push @lines, join "\t", $count, substr( $weight, 0, 8 ), $n;
	}
	return @lines;
}



sub dump_tdm {
	my ( $self, $file ) = @_;

	my $counter = 0;
	my %lookup;
	$lookup{$_} = $counter++ foreach $self->term_list;

	my @docs = $self->doc_list;

	my $fh;
	if ( defined $file ) {
		open $fh, "> $file" or croak
			"Could not open TDM output file: $!";
	} else {
		*fh = *STDOUT;
	}
	foreach my $doc ( @docs ) {
		my $n = $self->{neighbors}{$doc};

		my $row_count = scalar keys %{$n};
		print $fh $row_count;

		foreach my $t ( sort keys %{$doc} ) {
			my $index = $lookup{$t};
			my ( $weight, undef ) = split m/,/, $n->{$t};
			print $fh ' ', $index, ' ', $weight;
		}
		print $fh "\n";
	}
}



sub near_neighbors {
	my ( $self, $name, $type ) = @_;
	
	my $node = _nodeify( $type, $name );
	
	my $n = $self->{neighbors}{$node};
	
	my %found;
	foreach my $next ( keys %{$n} ) {
		foreach my $mynext ( keys %{ $self->{neighbors}{$next} }){
			$found{$mynext}++;
		}
	}
	delete $found{$node};
	return keys %found;
}


sub term_count {
	my ( $self, $doc ) = @_;
	if ( defined $doc ) {
		my $node = $self->{neighbors}{ _nodeify( 'D', $doc) };
		return 0 unless defined $node;
		return scalar keys %{$node};
	} else {
		return scalar grep /T:/, 
		keys %{ $self->{neighbors} };
	}
}


sub term_list {
	my ( $self, $doc ) = @_;

	my $node = ( defined $doc ?
				 $self->{neighbors}{ _nodeify( 'D', $doc) } :
				 $self->{neighbors}
			 );

	sort map { s/^T://o; $_ }
		 grep /^T:/, keys %{ $node };
}



sub word_count {

	my ( $self, $term ) = @_;

	my $n = $self->{neighbors}; # shortcut

	my $count = 0;
	my @terms;
	if ( defined $term ) {
		push @terms, $term;
	}	else {
		@terms = $self->term_list();
	}

	foreach my $term (@terms ) {
		$term = _nodeify( 'T', $term) unless $term =~/^T:/o;
		foreach my $doc ( keys %{ $n->{$term} } ) {
			( undef, my $lcount ) = split /,/, $n->{$term}{$doc};
			$count += $lcount;
		}
	}

	return $count;
}





sub search {
	my ( $self, @query ) = @_;	
	my @nodes = _nodeify( 'T', @query );
	my $results = $self->raw_search( @nodes );	
	my ($docs, $words) = _partition( $results );
	return ( $docs, $words);
}



sub simple_search {
	my ( $self, $query ) = @_;
	my @words = map { s/\W+//g; lc($_) }
				split m/\s+/, $query;	
	my @nodes = _nodeify( 'T', @words );
	my $results = $self->raw_search( @nodes );
	my ($docs, $words) = _partition( $results );
	my @sorted_docs = sort { $docs->{$b} <=> $docs->{$a} } keys %{$docs};
	return @sorted_docs;
}

sub find_by_title {	
	my ( $self, @titles ) = @_;
	my @found;
	my @docs = $self->doc_list();
	my $pattern = join '|', @titles;
	my $match_me = qr/$pattern/i;
	#warn $match_me, "\n";
	foreach my $d ( @docs ) {
	#	warn $d, "\n";
		push @found, $d if $d =~ $match_me;
	}
	return @found;
}


sub find_similar {
	my ( $self, @docs ) = @_;
	my @nodes = _nodeify( 'D', @docs );
	my $results = $self->raw_search( @nodes );
	my ($docs, $words) = _partition( $results );
	return ( $docs, $words);
}


sub merge {
	my ( $self, $type, $good, @bad ) = @_;
	croak "must provide a type argument to merge"
		unless defined $type;
	croak "Invalid type argument $type to merge [must be one of (D,T)]" 
		unless $type =~ /^[DT]/io;
	
	my $target  = _nodeify( $type, $good );
	my @sources = _nodeify( $type, @bad );
	
	my $tnode = $self->{neighbors}{$target};
	

	foreach my $bad_node ( @sources ) {
		#print "Examining $bad_node\n";
		next if $bad_node eq $target;
		my %neighbors = %{$self->{neighbors}{$bad_node}};
		
		foreach my $n ( keys %neighbors ) {
			
			#print "\t $target ($bad_node) neighbor $n\n";
			if ( exists  $self->{neighbors}{$target}{$n} ) {
				#print "\t\t$n has link to $bad_node\n";
				# combine the local counts for the term members of the edge
				my $curr_val = $tnode->{$n};
				my $aug_val  = $self->{neighbors}{$bad_node}{$n};
				my ($w1, $c1) = split m/,/, $curr_val;
				my ($w2, $c2) = split m/,/, $aug_val;
				my $new_count = $c1 + $c2;
				$curr_val =~ s/,\d+$/,$new_count/;
				$tnode->{$n} = $curr_val;
				
				
			} else {
				
				die "sanity check failed for existence test"
					if exists $self->{neighbors}{$target}{$n};
				
				my $val = $self->{neighbors}{$bad_node}{$n};
				
				#print "\tno existing link -- reassigning $target -- $n\n";
				# reassign the current value of this edge
			    
				$self->{neighbors}{$n}{$target} = $val;
				$self->{neighbors}{$target}{$n} = $val;
			}
			
			delete $self->{neighbors}{$bad_node}{$n};
			delete $self->{neighbors}{$n}{$bad_node};
		}
		delete $self->{neighbors}{$bad_node};
	}
}

sub mixed_search {
	my ( $self, $incoming ) = @_;

	croak "must provide hash ref to mixed_search method"
		unless defined $incoming &&
		ref( $incoming ) &&
		ref( $incoming ) eq 'HASH';

	my $tref = $incoming->{'terms'} || [];
	my $dref = $incoming->{'docs'}  || [];

	my @dnodes = _nodeify( 'D', @{$dref} );
	my @tnodes = _nodeify( 'T', @{$tref} );

	my $results = $self->raw_search( @dnodes, @tnodes );
	my ($docs, $words) = _partition( $results );
	return ( $docs, $words);
}


sub store {
	my ( $self, @args ) = @_;
	if ( $self->{'xs'} ) {
		croak "Cannot store object when running in XS mode.";
	} else {
		$self->SUPER::nstore(@args);
	}
}


# Partition - internal method.
# Takes a result set and splits it into two hashrefs - one for
# words and one for documents

sub _partition {
	my ( $e ) = @_;
	my ( $docs, $words );
	foreach my $k ( sort { $e->{$b} <=> $e->{$a} }
					keys %{ $e } ) {

		(my $name = $k ) =~ s/^[DT]://o;
		$k =~ /^D:/  ? 
			$docs->{$name} = $e->{$k}  :
			$words->{$name} = $e->{$k} ;
	}
	return ( $docs, $words );
}

# return a list of all neighbor nodes
sub _neighbors {
	my ( $self, $node ) = @_;
	return unless exists $self->{neighbors}{$node};
	return keys %{ $self->{neighbors}{$node} };
}


sub _nodeify {
	my ( $prefix, @list ) = @_;
	my @nodes;
	foreach my $item ( @list ) {
		push @nodes,  uc($prefix).':'.$item;
	}
	( wantarray ?  @nodes : $nodes[0] );
}



sub _read_tdm {
	my ( $self, $file ) = @_;
	print "Loading TDM...\n" if $self->{'debug'} > 1;

	croak "File does not exist" unless -f $file;
	open my $fh, $file or croak "Could not open $file: $!";
	for ( 1..4 ){
		my $skip = <$fh>;
	}
	my %neighbors;
	my $doc = 0;	


	######### XS VERSION ##############
	if ( $self->{'xs'} ) {

		my $map = $self->{'node_map'}; # shortcut alias
		while (<$fh>) {
			chomp;
			my $dindex = $self->_add_node( "D:$doc", 2 );
			#warn "Added node $doc\n";
			my ( $count, %vals ) = split;
			while ( my ( $term, $edge ) = each %vals ) {
				$self->{'term_count'}{$term}++;
				my $tnode = "T:$term";

				my $tindex = ( defined $map->{$tnode} ?
								$map->{$tnode} : 
							 	$self->_add_node( $tnode, 1 )
							);
				$self->{Graph}->set_edge( $dindex, $tindex, $edge );				
			}
			$doc++;
		}

	####### PURE PERL VERSION ##########
	} else {
		while (<$fh>) {
			chomp;
			my $dnode = "D:$doc";
			my ( $count, %vals ) = split;
			while ( my ( $term, $edge ) = each %vals ) {
				$self->{'term_count'}{$term}++;
				my $tnode = "T:$term";

				$neighbors{$dnode}{$tnode} = $edge.',1';
				$neighbors{$tnode}{$dnode} = $edge.',1';
			}
			$doc++;
		}
		$self->{'neighbors'} = \%neighbors;	
	}

	print "Loaded.\n" if $self->{'debug'} > 1;
	$self->{'from_TDM'} = 1;
	$self->{'doc_count'} = $doc;
}



# XS version only
#
# This sub maintains a mapping between node names and integer index
# values. 

sub _add_node {
	my ( $self, $node_name, $type ) = @_;
	croak "Must provide a type" unless $type;
	croak "Must provide a node name" unless $node_name;
	croak "This node already exists" if 
		 $self->{'node_map'}{$node_name};

	my $new_id = $self->{'next_free_id'}++;
	$self->{'node_map'}{$node_name} = $new_id;
	$self->{'id_map'}[$new_id] = $node_name;
	$self->{'Graph'}->add_node( $new_id, $type );

	return $new_id;
}



#
# 	INTERNAL METHODS
# 

# each node should have the same number of inbound
# and outbound links

sub check_consistency {

	my ( $self ) = @_;
	my %inbound;
	my %outbound;
	
	
	foreach my $node ( keys %{$self->{neighbors}} ) {
		next unless $node =~ /^[DT]:/; # for MLDBM compatibility
		$outbound{$node} = scalar keys %{$self->{neighbors}{$node}};
		foreach my $neighbor ( keys %{ $self->{neighbors}{$node} } )	{
			$inbound{$neighbor}++;
		}
	}
	
	my $in = scalar keys %inbound;
	my $out = scalar keys %outbound;
	carp "number of nodes with inbound links ($in) does not match number of nodes with outbound links ( $out )"
		unless scalar keys %inbound == scalar keys %outbound;
	
	foreach my $node ( keys %inbound ) {
		$outbound{$node} ||= 0;
		carp "$node has $inbound{$node} inbound links, $outbound{$node} outbound links\n"
			unless $inbound{$node} == $outbound{$node};
	}

}


sub have_edge {
	my ( $self, $node1, $node2 ) = @_;
	return exists $self->{neighbors}{$node1}{$node2};
}


{

	my %visited;
	my %component;
	my $depth;
	
	sub connected_components {
		my ( $self ) = @_;
		
		%visited = (); # clear any old info
		%component = ();
		
		
		my $n = $self->{neighbors};
		
		
		my @node_list =  keys %{$n};
		my @components;
		
		while ( @node_list ) {
			my $start = shift @node_list;
			next if exists $visited{$start};
			
			last unless $start;
			warn "Visiting neighbors for $start\n";
			visit_neighbors( $n, $start );
			push @components, [ keys %component ];
			 %component = ();
		}
		
		warn "Found ", scalar @components, " connected components\n";
		return @components;
		
		
	}

	sub visit_neighbors {
		my ( $g, $l ) = @_;
		return if $visited{$l};
		$depth++;
		$visited{$l}++; $component{$l}++;
		warn  $depth, "  $l\n";
		my @neigh = keys %{ $g->{$l} };		
		foreach my $n ( @neigh ) {
			visit_neighbors( $g, $n );
		}
		$depth--;
	}	
}


# Wipe the graph free of stored energies

sub _clear {
	my ( $self ) = @_;
	$self->{'energy'} = undef;
}


# Gather the stored energy values from the graph

sub _collect {
	my ( $self ) = @_;
	my $e = $self->{'energy'};
	my $result = {};
	foreach my $k ( keys %{$self->{'energy'}} ) {
		next unless $e->{$k} > $self->{'COLLECT_THRESHOLD'};
		$result->{$k} = $e->{$k};
	}
	return $result;
}




 #  Assign a starting energy ENERGY to NODE, and recursively distribute  the 
 #  energy to neighbor nodes.   Singleton nodes get special treatment 

sub _energize {

	my ( $self, $node, $energy ) = @_;


	return unless defined $self->{neighbors}{$node};
	my $orig = $self->{energy}{$node} || 0;
	$self->{energy}->{$node} += $energy;
	return if ( $self->{depth} == $self->{max_depth} );
	$self->{depth}++;

	if ( $self->{'debug'} > 1 ) {
		print '   ' x $self->{'depth'};
		print "$node: energizing  $orig + $energy\n";
	}


	my $n = $self->{neighbors};
	
	#sleep 1;
	my $degree = scalar keys %{ $n->{$node} };


	if ( $degree == 0 ) {
		
		carp "WARNING: reached a node without neighbors: $node at search depth $self->{depth}\n";
		$self->{depth}--;
		return;
	}
	
	
	my $subenergy = $energy / (log($degree)+1);


	# At singleton nodes (words that appear in only one document, for example)
	# Don't spread energy any further.  This avoids a "reflection" back and
	# forth from singleton nodes to their neighbors.

	if ( $degree == 1 and  $energy < $self->{'START_ENERGY'} ) {

		#do nothing

	} elsif ( $subenergy > $self->{ACTIVATE_THRESHOLD} ) {
		print '   ' x $self->{'depth'}, 
		"$node: propagating subenergy $subenergy to $degree neighbors\n"
		 if $self->{'debug'} > 1;
		foreach my $neighbor ( keys %{ $n->{$node} } ) {
			my $pair = $n->{$node}{$neighbor};
			my ( $edge, undef ) = split /,/, $pair;
			my $weighted_energy = $subenergy * $edge;
			print '   ' x $self->{'depth'}, 
			" edge $edge ($node, $neighbor)\n"
				if $self->{'debug'} > 1;
			$self->_energize( $neighbor, $weighted_energy );
		} 
	}	
	$self->{'depth'}--;	
	return 1;
}


# Given an array, normalize using cosine normalization

sub __normalize {
	my ( $arr ) = @_;

	croak "Must provide array ref to __normalize" unless
		defined $arr and
		ref $arr and
		ref $arr eq 'ARRAY';

	my $sum;
	$sum += $_->[2] foreach @{$arr};
	$_->[2]/= $sum foreach @{$arr};
	return 1;
}




sub DESTROY {
	undef $_[0]->{Graph}
}

1;

__END__

package Search::ContextGraph::SQLite;

use DBI;
use strict;
use warnings;

our %hash;

sub TIEHASH {
	my ( $class ) = @_;
	my $self = {};
	warn "Creating tied hash\n";
	my $dbh = DBI->connect("dbi:SQLite:dbname=test.db","","");
	if ( !-f "test.db" ) {
		my $sql = $dbh->do( "drop table edges; create table edges ( source char(100), sink char(100), weight float )" );
		my $sql = $dbh->do( "create index source on edges(source)" );
		my $sql = $dbh->do( "create index sink on edges(sink)" );
		my $sql = $dbh->do( "create unique index edge on edges(source, sink)" );
	}
	$self->{dbh} = $dbh;
	bless $self, $class;
}

sub FETCH {
	my ( $self, $key ) = @_;
	
	$self->{$key};	
}

sub STORE {
	my ( $self, $key, $value ) = @_;
	print "Storing key $key, value $value\n";
	#print ref $value, "\n";
	print "Hash has ", scalar %{$value}, "values\n";
	foreach my $k ( keys %{$value} ) {
		print "\t$k $value->{$k}\n";
	}
	$self->{$key} = $value
}

sub EXISTS {
	my ( $self, $key ) = @_;
	exists $self->{$key};
}

sub DELETE {
	my ( $self, $key ) = @_;
	delete $self->{$key};
}

sub FIRSTKEY { 
	my ( $self ) = @_;
	my $a = keys %{ $self };
	each %{ $self };
}

sub NEXTKEY {
	my ( $self ) = @_;
	each %{ $self };
}

sub DESTROY {}
	
1;


package Search::ContextGraph::TieWrapper;

use strict;
use warnings;

our %hash;

sub TIEHASH {
	my ( $class ) = @_;
	my $self = {};
	bless $self, $class;
}

sub FETCH {
	my ( $self, $key ) = @_;
	$self->{$key};	
}

sub STORE {
	my ( $self, $key, $value ) = @_;
	$self->{$key} = $value
}

sub EXISTS {
	my ( $self, $key ) = @_;
	exists $self->{$key};
}

sub DELETE {
	my ( $self, $key ) = @_;
	delete $self->{$key};
}

sub FIRSTKEY { 
	my ( $self ) = @_;
	my $a = keys %{ $self };
	each %{ $self };
}

sub NEXTKEY {
	my ( $self ) = @_;
	each %{ $self };
}

sub DESTROY {}
	
1;