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					 Using Structure Indices for
 Efficient Approximation of
     Network Properties
Matthew J. Rattigan, Marc Maier, and David Jensen
      University of Massachusetts Amherst

                  Data Mining
               November 27, 2006
                Deborah Stoffer
The Problem
   Recent research works with very large networks
       Millions of nodes
   Calculating network statistics on very large
    networks can be difficult
       Shortest paths
       Betweenness centrality
            The proportion of all shortest paths in the network that run
             through a given node
       Closeness centrality
            The average distance from the given node to every other node
             in the network
The Problem
   The most efficient known algorithms for
    calculating betweenness centrality and closeness
    centrality are O(ne + n2logn)
       n – number of nodes
       e – number of edges
   Calculations for path finding can have even
    higher complexity
       Require bidirectional breadth-first search
The Problem
   Example - Rexa citation graph
       Papers in computer science and related fields
       Largest connected component contains 165,000
        nodes (papers) and 321,000 edges (citations)
       Finding a path of length 15 requires the exploration of
        65,000 nodes
The Problem
Network Structure Index (NSI)
   Similar to the type of index commonly used to speed
    queries in modern database systems
   Can be constructed once for a given graph and then used
    to speed the calculations of many measures on the graph
   Two components of a NSI
       Set of annotations on every node in the network that provide
        information about relative or absolute location
            For G(V,E) the annotations define A: V → S, where S is an
             arbitrarily complex “annotation space”
       A distance function that uses the annotations to define graph
        distance between pairs of nodes by mapping pairs of node
        annotations to a positive real number
            D: S x S → R
Types of Network Structure Indices
 All Pairs Shortest Path (APSP)
 Degree
 Landmark
 Global Network Positioning (GNP)
 Zone
 Distance to Zone (DTZ)
All Pairs Shortest Path NSI
   Node annotations
       Consist of an n x n matrix (n = |V|) containing the
        optimal path distances between all pairs of nodes
   Distance function
       A simple lookup in the matrix
Degree NSI
   Node annotations
       Annotate each node with its undirected degree within
        the graph
   Distance function between source node s and
    target node t
       DDegree (s, t) = 2n – degree (s) – degree (t)
Landmark NSI
 Randomly designate a small number of nodes in
  the network to serve as navigational beacons
 Node annotations
       Annotate nodes in the graph by flooding out from
        each landmark and recording the graph distance to
        each node in the network
       Gives a vector of graph distances for each node
   Distance function
    
Landmark NSI
Global Network Positioning NSI
   Node annotation
       Annotation uses a nonlinear optimization algorithm
        to create a multidimensional coordinate system that
        encodes the location of each node within the network
   Distance function is the Manhattan distance
    between node pairs
    
Zone NSI
   Node annotations
       Each node is annotated with a d-dimensional vector of
        zone labels
   Distance function

    
Zone NSI Algorithm
   For d dimensions
       Randomly select k seed nodes, assign them zone
        labels 1 through k, and place them in the labeled set
       Place all other nodes in the unlabeled set
       While the unlabeled set is not empty
            Randomly select a node l from the labeled set
            Randomly select a node u from the unlabeled set that is a
             neighbor to l
            Assign u to the same zone as l and move it to the labeled set
Zone NSI
Distance to Zone (DTZ) NSI
 Hybrid between Landmark and Zone NSIs
 Node annotations
       Divide the graph into zones and for each node u and
        zone Z calculate the distance from u to the closest
        node in Z
   Distance function
    
Distance to Zone (DTZ) NSI
Complexity of Different NSIs
Search Performance
   Optimality of the lengths of paths found
       Path ratio

    


       pf is the length of the found paths
       po is the length of the optimal paths
       r is the number of randomly selected pairs of nodes in
        the graph
       P = 1.0 indicates an NSI that finds optimal paths
       P >> 1.0 indicates a poor performing NSI
Search Performance
   Performance gain
       Exploration ratio

    


       ef is the number of nodes explored by best-first search
       eb is the number of nodes that are explored using a
        bidirectional breadth-first search
       r is the number of pairs of nodes in the graph
       E values close to zero indicate good search performance
       E values greater than 1.0 indicate poor search
        performance
Search Performance
   NSIs evaluated on synthetic graphs
       Random
       Rewired lattices
       Forest Fire
Search Performance
Search Performance
Search Performance
Search Performance
Constant Time Distance Estimation
 Can sometimes use an NSI to directly estimate the
  graph distance between any two nodes
 Can use the DTZ annotation distance to estimate
  actual graph distances
       Annotate the graph as described for the DTZ NSI
       Randomly sample p pairs of nodes in the graph and
        perform breadth-first search to obtain their exact graph
        distance
       Use linear regression to obtain an equation for
        estimated distance
Constant Time Distance Estimation
Constant Time Distance Estimation
Constant Time Distance Estimation
   Simple distance can be used to produce a wide
    variety of attributes on nodes, which can be used
    by data mining algorithms that analyze graphs
       Label nodes with their distance to a particular node in a
        graph
            How close is each actor to Kevin Bacon?
       Label nodes with the minimum or maximum distance
        to one of a set of designated nodes
            How close is each actor to an Academy Award winner?
Closeness Centrality
   Measures the proximity of a given node in a
    network to every other node
    



 Important to social network dynamics
 Accurate estimates of closeness centrality often
  impossible to calculate for large data sets
 Using an NSI for path finding can estimate
  closeness centrality efficiently
Closeness Centrality
Closeness Centrality
   A measure of centrality can be used to produce
    attributes on nodes that may be useful to
    knowledge discovery algorithms
       Determine the closeness of every node to a collection
        of key nodes
            Closeness to all winners of Academy Awards for best actor in
             the past 10 years
       Constrain closeness calculations for members of
        clusters
            Closeness rank of an actor within their movie industry
       Weight closeness based on the attributes of the
        outlying nodes
            Closeness to winners of Academy Awards weighted by how
             recent an award
Betweenness Centrality
   Measures the number of short paths on which a
    given node lies
    



 Important to social network dynamics
 Accurate estimates of betweenness centrality
  often impossible to calculate for large data sets
Betweenness Centrality
 Can estimate betweenness using the paths
  identified through NSI navigation
 Randomly sample pairs of nodes and discover the
  shortest path between them
 Count the number of times each node in the graph
  appears on one of these paths to obtain a
  betweenness ranking
Betweenness Centrality
Betweenness Centrality
   A high betweenness score can indicate a bridge
    between two communities
       An actor that has played in movies belonging to
        different movie industries
   Betweenness centrality can be used to create
    features on nodes that are useful for data mining
       Calculate betweenness centrality for particular groups
        of nodes
            Actors that sit between winners of Academy Awards for best
             picture and the IMDb’s “Bottom 100”, the worst 100 movies as
             voted by users of the Internet Movie Database
Conclusions
 The NSIs Zone and DTZ allow efficient and
  accurate estimation of path lengths between
  arbitrary nodes in a network
 Efficient calculations of network statistics allow a
  better range of potential approaches to knowledge
  discovery
 All potential NSIs have not been exhaustively
  researched
 NSIs could have other applications
       Finding connection subgraphs
       Approximating neighborhood functions
Questions?

				
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