Visual abstract for "Simulating subject communities in case law citation networks"
This visual abstract is a graphical and layman summary of the key findings of the article Simulating Subject Communities in Case Law Citation Networks published in Frontier Physics, Volume 9, 2021, https://doi.org/10.3389/fphy.2021.665563
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.
This visual abstract was created with contributions from Tay Mui Yen, Dong Danping, and Aaron Tay from SMU Libraries
Confidential or personally identifiable information
- I confirm that the uploaded data has no confidential or personally identifiable information.