Abstract
We propose and evaluate generative models for case law citation networks that account for legal authority, subject relevance, and time decay. Since Common Law systems rely heavily on citations to precedent, case law citation networks present a special type of citation graph which existing models do not adequately reproduce. We describe a general framework for simulating node and edge generation processes in such networks, including a procedure for simulating case subjects, and experiment with four methods of modelling subject relevance: using subject similarity as linear features, as fitness coefficients, constraining the citable graph by subject, and computing subject-sensitive PageRank scores. Model properties are studied by simulation and compared against existing baselines. Promising approaches are then benchmarked against empirical networks from the United States and Singapore Supreme Courts. Our models better approximate the structural properties of both benchmarks, particularly in terms of subject structure. We show that differences in the approach for modelling subject relevance, as well as for normalizing attachment probabilities, produce significantly different network structures. Overall, using subject similarities as fitness coefficients in a sum-normalized attachment model provides the best approximation to both benchmarks. Our results shed light on the mechanics of legal citations as well as the community structure of case law citation networks. Researchers may use our models to simulate case law networks for other inquiries in legal network science.
Highlights
Citations between cases form the bedrock of Common Law reasoning, organizing the law into directed graphs ripe for network analysis
Examining case law citation networks (“CLCN”s) from the Supreme Courts of the United States, Canada, and India, Whalen et al [8] find that cases whose citations have low average ages, but high variance within those ages are significantly more likely to later become highly influential
We study by simulation the topological and community properties of networks produced by these alternative models (Section 3.1) and benchmark promising models against two empirical CLCNs: early decisions of the United States Supreme Court and of the Singapore Court of Appeal (Section 3.2)
Summary
Citations between cases form the bedrock of Common Law reasoning, organizing the law into directed graphs ripe for network analysis. Other factors shaping paper citations include age [31] and text similarity [32] These variables’ interacting influences on citation formation yield rich structural dynamics in citation networks. It is worth studying how CLCNs relate to traditional citation networks To this end, we examine how far generative models proposed for traditional citation networks can successfully replicate CLCNs. After a brief review of existing models (Section 2.2), we propose and evaluate a CLCN-tailored model that attempts to account for the unique mechanics of legal citations. We study by simulation the topological and community properties of networks produced by these alternative models (Section 3.1) and benchmark promising models (and baselines) against two empirical CLCNs: early decisions of the United States Supreme Court and of the Singapore Court of Appeal (Section 3.2).. Our work represents a first step towards better capturing and studying the mechanics of case law citation networks
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