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The European Community of Law and the Communities of Case-law: Understanding legal concepts and processes through the lens of community detection algorithms

Published online by Cambridge University Press:  09 December 2024

Urška Šadl
Affiliation:
European University Institute, Fiesole, Italy MOBILE, University of Copenhagen, Copenhagen, Denmark
Lucía López Zurita*
Affiliation:
MOBILE, University of Copenhagen, Copenhagen, Denmark
Sebastiano Piccolo
Affiliation:
University of Calabria, Rende, Italy
*
Corresponding author: Lucía López Zurita; Email: lucia.lopez.zurita@jur.ku.dk
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Abstract

Community detection is a set of algorithms developed in network science to find meaningful sub-groups within larger groups. This article (1) outlines and evaluates the method and (2) shows how it can enrich ongoing debates about European integration. To this end, it uses the example of the approximation of laws, an enduring topic in European legal studies.

Information

Type
Dialogue and debate: Symposium on New Interdisciplinary Perspectives in EU Law
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use.
Copyright
© The Author(s), 2024. Published by Cambridge University Press
Figure 0

Figure 1. Communities of case-law (clusters). Fastgreedy identifies 19 cohesive communities (clusters); of those four are large (rows 1, 4, 8, and 5).

Figure 1

Figure 2. Visualizes the effect of community detection on the Court’s citation network. The initial unordered adjacency matrix does not show any recognisable pattern. By applying community detection algorithms and discovering communities in data, the patterns become evident, and each algorithm shows some specific aspect of the data. Fastgreedy divides the networks into four big communities that account for ∼10000 nodes (∼85 per cent of the network) and a bunch of smaller ones. Louvain finds 18 communities plus some other small ones. Louvain mainly breaks down the four biggest communities from Fastgreedy into smaller ones. Label propagation finds a very large community which clusters together the first and the third largest communities found by Fastgreedy, another community which corresponds to the second largest community from Fastgreedy and a third smaller community that is a subset of the fourth community found by Fastgreedy. Walktrap highlights a core-periphery organisation of the network with one large community serving as a core and other 12 smaller communities followed by 550+ micro-communities. Infomap clusters the network in a large number (700+) of small communities. For all algorithms, it is clear how the detected clusters have higher internal density of connections than they have externally. Furthermore, the smaller a community, the higher its internal density.

Figure 2

Table 1. Table 1 compares the communities discovered by the five different algorithms: Fastgreedy (FG), Louvain (LM), Label propagation (LP), Walktrap (WT), Infomap (IM). A) Adjusted Mutual Information, B) Adjusted Rand Index, C) Fowlkes-Mallows score, D) Matthews Correlation Coefficient, E) Informedness, F) Markedness.

Figure 3

Figure 3. Shows the hierarchical organization of the Court’s citation network. The co-occurrence matrix shows how the clusters found by the algorithms are nested into each other (left square). Hierarchy-ALL shows the adjacency matrix of the Court’s case-law reordered according to the hierarchical order suggested by all the algorithms (middle square). Hierarchy F-L, shows the adjacency matrix reordered according to the hierarchical order suggested by Fastgreedy and Louvain (right square).

Figure 4

Table 2 The frequent subject matter of judgements in selected significant communities found by Fastgreedy (left column) and Louvain (right column). Some labels are identical, meaning that the communities overlap entirely. For instance, Fastgreedy community C15 includes the label ‘common customs tariff’ which corresponds to an identical Louvain community C23. Most communities, however, discovered by Fastgreedy and Louvain are similar with respect to the labels they include, such as the community dealing with taxation (third row).

Figure 5

Figure 4. Displays the most frequent labels in the community ‘ECSC’.

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Figure 5. Displays the most frequent labels in the community ‘taxation’.

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Figure 6. Displays the most relevant labels in community ‘Customs Union’.

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Figure 7. Displays the most frequent labels in the community ‘social policy’.

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Figure 8. Displays the most frequent labels in the community ‘free movement of agricultural products’.

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Figure 9. Displays the most frequent labels in the community ‘the regulation affecting intellectual property’.

Figure 11

Figure 10. Displays the most frequent labels in the community ‘politically significant policies’.

Figure 12

Figure 11. Displays the most frequent labels in the community ‘Legal basis/Article 115 TFEU’.

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Figure 12. Displays the most frequent labels in the community ‘harmonized market’.