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Python source code of JacSim*, a link-based similarity measure providing a solution to the pairwise normalization problem in SimRank

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JacSim*: An Effective and Efficient Solution to the Pairwise Normalization Problem in SimRank

This repository provides the Python implementations of JasSim*, both Matrix form and Iterative form.

Installation and usage

JacSim* is a recursive link-based similarity measure, which is applicable to both directed and undirected graphs. In the case of directed graphs, similarity scores can be computed based on any of in-links or out-links. In order to use JacSim*, the following packages are required:

Python       >= 3.8
networkx     =2.6.*
numpy        =1.21.*
scikit-learn =1.0.*

Graph file format: A graph must be represented as a text file under the edge list format in which, each line corresponds to an edge in the graph, tab is used as the separator of the two nodes, and the node index is started from 0.

Citation:

Masoud Reyhani Hamedani and Sang-Wook Kim. 2021. JacSim*: An Effective and Efficient Solution to the Pairwise Normalization Problem in SimRank. IEEE Access, Vol. 9, pages= 146038-146049. https://doi.org/10.1109/ACCESS.2021.3123114

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Python source code of JacSim*, a link-based similarity measure providing a solution to the pairwise normalization problem in SimRank

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