Analysis of reference and citation copying in evolving bibliographic networks. Issue 1 (February 2020) - Pandey, Pradumn Kumar; Singh, Mayank; Goyal, Pawan; Mukherjee, Animesh; Chakrabarti, Soumen
Highlights: We propose Ref Or Cite, a new model that allows for copying of both the references made from (out-neighbors), as well as the citations made to (in-neighbors) a paper. We leverage four popular large-scale citation networks to showcase the effectiveness of Ref Or Cite . Empirically and analytically, Ref Or Cite, matches the degree distribution better and also generates number of triangles closer to that in real data. Abstract: Extensive literature demonstrates how the copying of references (links) can lead to the emergence of various structural properties (e.g., power-law degree distribution and bipartite cores) in bibliographic and other similar directed networks. However, it is also well known that the copying process is incapable of mimicking the number of directed triangles in such networks; neither does it have the power to explain the obsolescence of older papers. In this paper, we propose Ref Or Cite, a new model that allows for copying of both the references from (i.e., out-neighbors of) as well as the citations to (i.e., in-neighbors of) an existing node. In contrast, the standard copying model (CP) only copies references. While retaining its spirit, Ref Or Cite differs from the Forest Fire (FF) model in ways that makes Ref Or Cite amenable to mean-field analysis for degree distribution, triangle count, and densification. Empirically, Ref Or Cite gives the best overall agreement with observed degree distribution, triangle count, diameter, h-index, and the growth of citations to newer papers.