Exploratory analysis of legal case citation data using node embedding
subject: Graph embedding; Legal citation network; Network analysis; Node embed-ding, Node2vec
description: 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.
creator: Lodha S.; Wagh R.
source: ICIC Express Letters, Vol-13, No. 10, pp. 883-889.
publisher: ICIC International
date: 2019-01-01
rights: Restricted Access
relation: ISSN: 1881803X
format: Online
language: English
type: Article
coverage: 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