In this abstract we introduce the working context related to the understanding of an heterogeneous network of references contained in the Italian regulatory framework. We then present an extended analysis of a large network of laws, providing several types of analytical evaluation that can be used within a legal management system for understanding the data through summarization, visualization, and browsing.
The importance of Information Technology tools lies on the ability of limiting the well-known complexity of the regulatory framework. Starting from the language used to express concepts and rules that often create serious problems of interpretation, scientia iuris is full of innumerable aspects that make it difficult to manage by domain experts. The use of computer systems can reduce this complexity for those who have to do with a legal reality that results to be more and more interdisciplinary, international and multifunctional. All this is even more true by taking into account one of the most complex aspects of any modern legal text, which is the extensive use of references. It is therefore the intention of this paper to first investigate the need for the development of tools capable of assisting the employee to manage the particular regulatory complexity characterized by cross-references, and then to simulate possible technological solutions.
The presence of thousands of stratifications in the Italian legal sources over the years has enabled the strengthening of the use of cross-references between legal texts in order to complete the content. This scenario is even more complex when considering a second and unique aspect of this discussion: the temporal dynamism that characterizes the use of normative references in any legal context. In fact, it is not a rarity that, over the years, the legislature put hand to the subjects adapting the contents to the stimuli given by socio-economic factors and the surrounding cultural context. This natural evolution is often mediated through a surgical use of references which permit to individuate the precise points under modification. In the Italian scenario, this assumption is far from being uncommon. Many, in fact, are the examples of legal decrees subsequently converted (even with modifications) into laws. Finally, it is worth noting how the use of normative references can also take place outside the purely legislative context. In fact, it is quite common to find a citation of a law inside a legal text that is not used with explicit references, but also by case law of the courts, which, in exercising a peculiar hermeneutical activity of this rule, it implicitly complements the content, clarifying the more conceptual and obscure aspects and thus bringing important suggestions to the reader.
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In legislation, all the data can be also viewed as complex networks where nodes are laws and links represent kinds of relationship like “modification”, “implementation”, “substitution”, and so on. These information are often complex to treat, organize, and use since they are many and continuosly changing over time. Manual intervention, indeed, is often required in classical tasks because of the difficulty to get the “big picture”, that is, to have an at-a-glance overview over the data under evaluation.
Social Network Analysis is quite a new field that inherits methods from Phisics
(i.e., Complex Networks) and Mathematics (i.e., Theory of Graphs) to face problems
related to the huge amount of data coming from social networks like Twitter2, Facebook3,
Flickr4, and so forth. These algorithms are useful to capture statistics about the type of
the networks along their properties. More in detail, it is possible to analyze a network
in terms of its evolution over time, important nodes, implicit relationships, etc. All these
evaluations can be helpful also in a network of laws. [
The first statistical analysis of a graph is given by the distribution of the number of neighbours of a node, also called degree. In real networks, the degree distribution has a tail that often follows a power law, that means that it contains many nodes with low degree and some node with large degree. This characteristic of real networks is called community structure. Such communities are also commonly named clusters, and they represent nodes that play similar roles within the graph. A common and often useful analysis of a network looks at such latent community structures, and it is based on some clustering approach. In our test we made use of the concept of edge betweenness of an edge, i.e., the extent to which it lies along shortest paths between all pairs of nodes in the network. This algorithm works by iteratively following two steps: computing the edge betweenness for all edges in the current graph and then removing the edge with the highest betweenness value. This analysis helps finding communities, since it iteratively removes central nodes to separate the graph in distinct subnetworks.
Relationships between nodes of a network may have a precise direction, that needs to
be taken into account to understand the system as a whole. PageRank [
The diameter of a graph can give useful insights. In detail, it is the largest number of nodes which must be traversed in order to travel from one node to another. In a network of laws it can be used to estimate the maximum complexity of modification/citation paths. Since a network of law is often disconnected, it is constituted by many subnetworks. In this case, the diameter considers the maximum distance found in all subnetworks.
In this section we present an analysis of the data coming from Eunomos, i.e., a network of around 10K laws interconnected by 7K links. The dataset contains different types of laws, as for instance “stato:legge”, that indicates the principal type of law of Italy. The resulting distribution of the degree levels demonstrated that most of the laws have few connections with other laws, whereas some other laws have a large connectivity within the graph. This is in line with the majority of the networks automatic systems have to deal with.
The diameter of the network (actually, the maximum diameter of all the disconnected subnetworks in it) is 8. This means that, in the worst case, if a jurist has to navigate and understand a path between two laws in the network, it could go through other eight laws. Still, links can have different meanings, so the process may include multiple cateogories of operations. Having a tool that can support this navigation may represent a dramatically help in such process.
Finally, the distribution of the PageRank scores over the nodes/laws of the network gives an interesting result since it seems to perfectly split the important nodes (also called hubs) from the others. A system that makes use of these scores can filter out unimportant information rather than letting emerge crucial points within the regulatory framework.