Prediction of new scientific collaborations through multiplex networks

Abstract

The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks.

The dynamic resilience of urban labour networks

“…geo-industrial networks [8] and supply chain networks [61]. It is likely that the true dynamics involve some combination of these into a multiplex network [62], on which the cooperative spreading of more than one social norm would diffuse [63,64]. More related studies of the diffusion mechanism on labour market networks could bring deeper understanding on them.…”

The Dynamic Resilience of Urban Labour Networks

“…geo-industrial networks [11] and supply chain networks [43]. It is likely that the true dynamics involve some combination of these into a multiplex network [44], on which the cooperative spreading of more than one social norms would diffuse [45,46]. More related studies of the diffusion mechanism on labour market networks could bring deeper understanding on them.…”

Link Prediction for Statistical Collaboration Networks Incorporating Institutes and Research Interests

“…Therefore, it is important to develop techniques to generate collaborative recommendations. In the context of network science, recommending partnerships is type of a link prediction problem [11]. Link prediction is the task of estimating the likelihood of an unobserved link between two nodes [12], [13].…”

“…In the field of quantum communication, Lande et al [15] use authors' information and keywords in articles to predict collaboration among scientists. Instead of focusing on a particular discipline, Tuninetti et al [11] restrict their analysis to papers published in Physical Review Letters. They demonstrate that scientific credit and common scientific interests are predictive of new collaborations among scientists.…”

“…For example, Bhattacharyya et al [20] study the similarity of users based on keywords from their profiles. Tuninetti et al [11] use mutual citations and common keywords to predict collaboration between two authors. Both topology-based and node-based metrics provide abundant information for link prediction from different perspectives.…”