In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification.

In this paper, we investigate an interpretable definition of promising research topics, complemented with a predictive model. Two methods of topic identification were employed: bag of words and the LDA model, with reflection on their applicability and usefulness in the task of retrieving topics on a set of publication titles. Next, different criteria for promising topic were analyzed with respect to their usefulness and shortcomings. For verification purposes, the DBLP data set, an online open reference of computer science publications, is used. The presented results reveal potential of the proposed method for identification of promising research topics.