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See:
Description
Interface Summary | |
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EdgeScorer<E,S> | An interface for algorithms that assign scores to edges. |
VertexScorer<V,S> | An interface for algorithms that assign scores to vertices. |
Class Summary | |
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AbstractIterativeScorer<V,E,T> | An abstract class for algorithms that assign scores to vertices based on iterative methods. |
AbstractIterativeScorerWithPriors<V,E,S> | An abstract class for iterative random-walk-based vertex scoring algorithms that have a fixed probability, for each vertex, of 'jumping' to that vertex at each step in the algorithm (rather than following a link out of that vertex). |
BarycenterScorer<V,E> | Assigns scores to each vertex according to the sum of its distances to all other vertices. |
BetweennessCentrality<V,E> | Computes betweenness centrality for each vertex and edge in the graph. |
ClosenessCentrality<V,E> | Assigns scores to each vertex based on the mean distance to each other vertex. |
DegreeScorer<V> | Assigns a score to each vertex equal to its degree. |
DistanceCentralityScorer<V,E> | Assigns scores to vertices based on their distances to each other vertex in the graph. |
EigenvectorCentrality<V,E> | Calculates eigenvector centrality for each vertex in the graph. |
HITS<V,E> | Assigns hub and authority scores to each vertex depending on the topology of the network. |
HITS.Scores | Maintains hub and authority score information for a vertex. |
HITSWithPriors<V,E> | A generalization of HITS that permits non-uniformly-distributed random jumps. |
KStepMarkov<V,E> | A special case of PageRankWithPriors in which the final scores
represent a probability distribution over position assuming a random (Markovian)
walk of exactly k steps, based on the initial distribution specified by the priors. |
PageRank<V,E> | Assigns scores to each vertex according to the PageRank algorithm. |
PageRankWithPriors<V,E> | A generalization of PageRank that permits non-uniformly-distributed random jumps. |
VoltageScorer<V,E> | Assigns scores to vertices according to their 'voltage' in an approximate solution to the Kirchoff equations. |
Mechanisms for assigning values (denoting significance, influence, centrality, etc.) to graph elements based on topological properties. These include:
BarycenterScorer
: assigns a score to each vertex according to
the sum of the distances to all other vertices
ClosenessCentrality
: assigns a score to each vertex based on
the mean distance to each other vertex
DegreeScorer
: assigns a score to each vertex based on its degree
EigenvectorCentrality
: assigns vertex scores based on
long-term probabilities of random walks passing through the vertex at time t
PageRank
: like EigenvectorCentrality
, but with
a constant probability of the
random walk restarting at a uniform-randomly chosen vertex
PageRankWithPriors
: like PageRank
, but with a
constant probability of the random
walk restarting at a vertex drawn from an arbitrary distribution
HITS
: assigns hubs-and-authorities scores to vertices based on
complementary random walk processes
HITSWithPriors
: analogous to HITS
(see PageRankWithPriors
)
VoltageScorer
: assigns scores to vertices based on simulated
current flow along edges
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