Evaluating cooperation in citation datasets using core structures

Community subgraphs are characterized by dense connections or interactions among their nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities capitalizing on the k-core structure, as means of evaluating their collaborative nature – a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core concept, which essentially measures the robustness of a community under degeneracy, we extend it to graphs with weighted edges, devising the novel concept of fractional core for undirected graphs with edge-weighted edges. We applied these approaches to large real-world graphs investigating the co-authorship case for citation datasets from Computer Science (DBLP) and High Energy Physics (ARXIV.hep-th). Our findings are intuitive and we report interesting results and observations with regards to collaboration among authors. Evaluating cooperation in citation datasets using core structures 2