Efficient Algorithms for Citation Network Analysis
Vladimir Batagelj (University of Ljubljana, Slovenia)

TL;DR
This paper introduces efficient, linear-time algorithms for analyzing citation networks, focusing on computing specific arc weights and identifying important subnetworks, with applications to scientific literature and patents.
Contribution
It presents novel linear algorithms for arc weight computation and a method to identify key subnetworks based on these weights in citation networks.
Findings
Algorithms are linear in the number of arcs.
Theoretical properties of arc weights are established.
Practical applications demonstrated on citation networks of SOM literature and US patents.
Abstract
In the paper very efficient, linear in number of arcs, algorithms for determining Hummon and Doreian's arc weights SPLC and SPNP in citation network are proposed, and some theoretical properties of these weights are presented. The nonacyclicity problem in citation networks is discussed. An approach to identify on the basis of arc weights an important small subnetwork is proposed and illustrated on the citation networks of SOM (self organizing maps) literature and US patents.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRough Sets and Fuzzy Logic · Electrochemical Analysis and Applications · Advanced Algebra and Logic
