Topological analysis of citation networks to discover the future core articles

the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a " similar " position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness cen-trality, and betweenness centrality. We also analyzed the effect of aging as well as of self-correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain.