Exploratory analysis of legal case citation data using node embedding
Graph embedding; Legal citation network; Network analysis; Node embed-ding, Node2vec
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019.
ICIC Express Letters, Vol-13, No. 10, pp. 883-889.
https://doi.org/10.24507/icicel.13.10.883 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072955918&doi=10.24507%2ficicel.13.10.883&partnerID=40&md5=38f6f4a293acd4e6eb63fd2bd7b514aa
Lodha S., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560029, India; Wagh R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560029, India