Identifying influential patents in citation networks using enhanced VoteRank centrality

TL;DR

This paper enhances the VoteRank algorithm to better identify influential patents in citation networks by introducing distance-based voting reductions, improving the selection of key influence spreaders.

Contribution

The paper introduces two novel VoteRank-based algorithms, VoteRank-LRed and VoteRank-XRed, which incorporate distance-based voting reduction to improve influence spreader identification.

Findings

VoteRank-LRed outperforms original VoteRank in influence spreader detection

Enhanced algorithms effectively incorporate distance effects in citation networks

Demonstrated improved efficiency in influence maximization tasks

Abstract

This study proposes the usage of a method called VoteRank, created by Zhang et al. (2016), to identify influential nodes on patent citation networks. In addition, it proposes enhanced VoteRank algorithms, extending the Zhang et al. work. These novel algorithms comprise a reduction on the voting ability of the nodes affected by a chosen spreader if the nodes are distant from the spreader. One method uses a reduction factor that is linear regarding the distance from the spreader, which we called VoteRank-LRed. The other method uses a reduction factor that is exponential concerning the distance from the spreader, which we called VoteRank-XRed. By applying the methods to a citation network, we were able to demonstrate that VoteRank-LRed improved performance in choosing influence spreaders more efficiently than the original VoteRank on the tested citation network.