Using neural networks to study networks of scientific journals

In this paper a new approach to study science dynamics is introduced. This approach is based in the use of Kohonen preserving topology maps, a kind of neural network. Four data set consisting in cross-citation matrix are studied using this approach. Relations maps and domains maps are computed for these data sets and interrelationships among journals are studied. This approach allow to stude both, hierarchical journal structure in a given time and evolution of relations among journals in a given time lag.

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