This page appears to describe an older version of the Bibliographic Coup= ling algorithm. Some details may differ from the version that is currently = available.

Extracts a bibliographic coupling network from a network.

=20Can return sensible similarity networks based on citation, but may not m= ake sense for other kinds of networks.

=20Extracting similarity between nodes. Good for then feeding into force di= rected algorithms, especially DrL.

=20The algorithm in Network Workbench will take in a directed, undirected, = or hypergraph network. The undirected edges will be transformed into two di= rected edges. All edge weights are ignored. A new network is returned with = the same nodes (and their attributes) with weighted undirected edges where = each edge corresponds to a single similarity score (ranging from 0.0 to 1.0= ) between two nodes. The similarity score uses the following formula: simil= arity =3D sharedCitations / sqrt(citationCount1*citationCount2) where share= dCitations is the number of citations which they both cite, citationCount1 = is the total number of citations the first node cited, and citationCount2 i= s the total number of citations the second node cited. This algorithm takes= a single parameter, which states the maximum number of top bibliographic c= oupling scores to keep. If 0 is specified, then every possible bibliographi= c coupling score will be kept.

=20Any network can be given to this algorithm, though it makes most sense o= n directed networks, especially citation networks.

=20Todd Holloway implemented the first version in Perl. Bruce Herr later re= implemented the algorithm in C++ for scalability and speed. The C++ version= is in Network Workbench.

=20**References**

**Links**

- =20
- Source Code =20

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