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2021-11-29
Somsakul, Supawit, Prom-on, Santitham.  2020.  On the Network and Topological Analyses of Legal Documents Using Text Mining Approach. 2020 1st International Conference on Big Data Analytics and Practices (IBDAP). :1–6.
This paper presents a computational study of Thai legal documents using text mining and network analytic approach. Thai legal systems rely much on the existing judicial rulings. Thus, legal documents contain complex relationships and require careful examination. The objective of this study is to use text mining to model relationships between these legal documents and draw useful insights. A structure of document relationship was found as a result of the study in forms of a network that is related to the meaningful relations of legal documents. This can potentially be developed further into a document retrieval system based on how documents are related in the network.
2014-10-24
Baras, J.S..  2014.  A fresh look at network science: Interdependent multigraphs models inspired from statistical physics. Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on. :497-500.

We consider several challenging problems in complex networks (communication, control, social, economic, biological, hybrid) as problems in cooperative multi-agent systems. We describe a general model for cooperative multi-agent systems that involves several interacting dynamic multigraphs and identify three fundamental research challenges underlying these systems from a network science perspective. We show that the framework of constrained coalitional network games captures in a fundamental way the basic tradeoff of benefits vs. cost of collaboration, in multi-agent systems, and demonstrate that it can explain network formation and the emergence or not of collaboration. Multi-metric problems in such networks are analyzed via a novel multiple partially ordered semirings approach. We investigate the interrelationship between the collaboration and communication multigraphs in cooperative swarms and the role of the communication topology, among the collaborating agents, in improving the performance of distributed task execution. Expander graphs emerge as efficient communication topologies for collaborative control. We relate these models and approaches to statistical physics.