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 map s are computed for these data sets and interrelationships among journa ls are studied. This approach allow to stude both, hierarchical journa l structure in a given time and evolution of relations among journals in a given time lag.