Class for performing Hits algorithm.
Stores all values necessary to fully describe one Hits iteration
Stores all values necessary to fully describe one Hits iteration
Array of values indexed by node id storing hubs values for each node
Array of values indexed by node id storing authorities values for each node
The T1 error for the current iteration vs the previous iteration.
Stores all parameters for Hits algorithm
Stores all parameters for Hits algorithm
The maximum number of times that the link analysis algorithm will run before termination
The maximum error allowed.
Flag true
to return normalized values
The base class for all iterations through our iterative algorithms.
The base class for all iterations through our iterative algorithms. These classes will simply hold all of the information needed to assess the number of iterations, the error, and the current set of values.
All link analysis algorithms should inherit from the LinkAnalysis
base class.
All link analysis algorithms should inherit from the LinkAnalysis
base class.
LinkAnalysis
must be generically typed by IterationState
or one of its subclasses. An IterationState
holds all of the pertinent information for a given algorithm.
PageRank is a link analysis algorithm designed to measure the importance of nodes in a graph.
PageRank is a link analysis algorithm designed to measure the importance of nodes in a graph. Popularized by Google.
Unoptimized for now, and runs in a single thread.
Class containing information to fully describe a page rank iteration.
Class containing information to fully describe a page rank iteration.
The current set of pageRank values
The T1 error for the current iteration vs the previous iteration.
Parameters for PageRank
Parameters for PageRank
Probability of NOT randomly jumping to another node
The maximum number of times that the link analysis algorithm will run before termination
The maximum error allowed.
The base class for all parameters fed to our iterative algorithms.
Class for performing Hits algorithm. Hits is a link analysis algorithm that returns two values characterizing each node. Each node receives both a hub value and an authority value. A node that is characterized by a large hub value is one that has many high quality outbound connections to other nodes, while a node that is characterized by a large authority value has inbound connections from high quality hub nodes.