Publication

Abstract : Profuse growth of scientometrics as a research field owes a discernible attribution to the introduction of citation networks and other scientograms. Centrality analysis, path analysis and cluster analysis are three major network analysis tools. Hummon and Doreian’s introduction of path retrieval methods based on (1) traversal count as weight assignment (for arcs) method and (2) search methods such as local (forward) search and global search, marked the commencement of path analysis. Original Hummon–Doreian traversal count based weight assignment methods such as Search Path Link Count and Search Path Node Pair were computationally complex. Along with the computational improvement of these weights, Batagelj added another computationally efficient traversal count method to the path analysis literature known as Search Path Count. A major development in search methods was seen recently with the introduction of innovative search methods such as backward (local) search and key-route (local and global) search by Liu and Lu. They also powered the available and new local search methods with a parameter to control the search. Major advantage of Liu–Lu methods lies in the fact that these can reveal more paths or more papers that are usually missed out in classical methods. All these contributions considered unweighted citation networks as the object of analysis. Despite being a tool of tremendous potential, path analysis is much underexplored relative to other network analysis tools. Inspired by these, we generalise Liu–Lu integrated approach, the present state-of-art in path analysis to an integrated approach for weighted networks. We demonstrate a manifold improvement in analysis opportunities with the generalized integrated approach using FV gradient weights for weight assignment, on a case study of the field ‘IT for engineering’. Integrated approach for weighted networks do not need additional implementation effort in PAJEK and this will be beneficial for a multitude of analysts and decision makers.