Socio-Emotional intelligence: A humanising approach to enhance wellbeing in higher education

Authors: Devis-Rozental, C.

Pages: 15-33

DOI: 10.1007/978-3-030-57430-7_2

Abstract:

Comparative network analysis provides an effective computational means to identify the similarities and differences between biological networks so that it helps transferring the prior knowledge across different biological networks. Since identifying the optimal biological network alignment is practically infeasible due to the computational complexity, a number of heuristic network alignment algorithms have been proposed. Among various heuristic approaches, comparative network analysis methods using random walk models are very effective to construct a reliable network alignment because it can accurately estimate the node correspondence by integrating node similarity and topological similarity. In this chapter, we introduce effective random walk models and their applications in developing comparative network analysis algorithms.

Source: Scopus