Fabien Tarissan

Self-assembling Trees

RCCS is a variant of Milners CCS where processes are allowed a controlled form of backtracking. It turns out that the RCCS reinterpretation of a CCS process is equivalent, in the sense of weak bisimilarity, to its causal transition system in CCS. This can be used to develop an efficient method for designing distributed algorithms, which we illustrate here by deriving a distributed algorithm for assembling trees. Such a problem requires solving a highly distributed consensus, and a comparison with a traditional CCS-based solution shows that the code we obtain is shorter, easier to understand, and easier to prove correct by hand, or even to verify.

Impact of power-law topology on IP-level routing dynamics: Simulation results

This paper focuses on the Internet IP-level routing topology and proposes relevant explanations to its apparent dynamics.We first represent this topology as a power-law random graph. Then, we incorporate to the graph two well known factors responsible for the observed dynamics, which are load balancing and route evolution. Finally, we simulate on the graph traceroute-like measurements. Repeating the process many times, we obtain several graph instances that we use to model the dynamics. Our results show that we are able to capture on power-law graphs the dynamic behaviors observed on the Internet. We find that the results on power-law graphs, while qualitatively similar to the one of Erdös-Rényi random graphs, highly differ quantitatively; for instance, the rate of discovery of new nodes in power-law graphs is extremely low compared to the rate in Erdös-Rényi graphs.

Analysing the first case of the International Criminal Court from a network-science perspective

This paper analyses the multi-level network composed of the legal decisions taken by the International Criminal Court since its creation in 2002. As many real-world networks, legal networks lend themselves to the use of graphs in which nodes represent the decisions taken by the Court and links stand for citations between decisions. Although useful, this framework does not account for the inherent complexity and hierarchy commonly observed in real data. In the context of legal networks in particular, interactions between decisions take place at various levels, inducing a two-level structure. We propose here to rely on a hybrid version of bipartite graphs, which allows to represent different types of links in multi-level networks. We assess the relevance of this approach by analysing the hybrid structure of the first case of the Court and by confronting it with standard approaches focusing on direct citation processes. We validate the outcomes by providing juridical interpretations of the results, which shed some light on the procedural aspects of the International Criminal Court and put an emphasis on the key themes addressed by this jurisdiction. Thus, for the first time, this work converges two very different approaches to account for the multi-level complexity in legal networks. Complex networks, bipartite graph, legal network, International Criminal Court.

Revealing intricate properties of communities in the bipartite structure of online social networks

Many real-world networks based on human activities exhibit a bipartite structure. Although bipartite graphs seem appropriate to analyse and model their properties, it has been shown that standard metrics fail to reproduce intricate patterns observed in real networks. In particular, the overlapping of the neighbourhood of communities is difficult to capture precisely. In this work, we tackle this issue by analysing the structure of 4 real-world networks coming from online social activities. We first analyse their structure using standard metrics. Surprisingly, the clustering coefficient turns out to be less relevant than the redundancy coefficient to account for overlapping patterns. We then propose new metrics, namely the dispersion and the monopoly coefficients, and show that they help refining the study of bipartite overlaps. Finally, we compare the results obtained on real networks with the ones obtained on random bipartite models. This shows that the patterns captured by the redundancy and the dispersion coefficients are strongly related to the real nature of the observed overlaps.

Temporal properties of legal decision networks: a case study from the International Criminal Court

Many studies have proposed to apply artificial intelligence techniques to legal networks, whether it be for highlighting legal reasoning, resolving conflict or extracting information from legal databases. In this context, a new line of research has recently emerged which consists in considering legal decisions as elements of complex networks and conduct a structural analysis of the relations between the decisions. It has proved to be efficient for detecting important decisions in legal rulings. In this paper, we follow this approach and propose to extend structural analyses with temporal properties. We define in particular the notion of relative in-degree, temporal distance and average longevity and use those metrics to rank the legal decisions of the two first trials of the International Criminal Court. The results presented in this paper highlight non trivial temporal properties of those legal networks, such as the presence of decisions with an unexpected high longevity, and show the relevance of the proposed relative in-degree property to detect landmark decisions. We validate the outcomes by confronting the results to the one obtained with the standard in-degree property and provide juridical explanations of the decisions identified as important by our approach.

Comparing Overlapping Properties of Real Bipartite Networks

Many real-world networks lend themselves to the use of graphs for analysing and modelling their structure. But such a simple representation has proven to miss some important and non trivial properties hidden in the bipartite structure of the networks. Recent papers have shown that overlapping properties seem to be present in bipartite networks and that it could explain better the properties observed in simple graphs. This work intends to investigate this question by studying two proposed metrics to account for overlapping structures in bipartite networks. The study, conducted on four dataset stemming from very different contexts (computer science, juridical science and social science), shows that the most popular metrics, the clustering coefficient, turns out to be less relevant that the recent redundancy coefficient to analyse intricate overlapping properties of real networks.

Measuring the degree distribution of routers in the core internet

Most current models of the internet rely on knowledge of the degree distribution of its core routers, which plays a key role for simulation purposes. In practice, this distribution is usually observed directly on maps known to be partial, biased and erroneous. This raises serious concerns on the true knowledge one may have of this key property. Here, we design an original measurement approach targeting reliable estimation of the degree distribution of core routers, without resorting to any map. It consists in sampling random core routers and precisely estimate their degree thanks to probes sent from many distributed monitors. We run and assess a large-scale measurement following this approach, carefully controlling and correcting bias and errors encountered in practice. The estimate we obtain is much more reliable than previous knowledge, and it shows that the true degree distribution is very different from all current assumptions.

Self-assembling graphs

A self-assembly algorithm for synchronising agents and have them arrange according to a particular graph is given. This algorithm, expressed using an ad hoc rule-based process algebra, extends Klavins’ original proposal (Klavin, 2002: Automatic synthesis of controllers for assembly and formation forming. In: Proceedings of the International Conference on Robotics and Automation), in that it relies only on point-to-point communication, and can deal with any assembly graph whereas Klavins’ method dealt only with trees.

UDP Ping: A Dedicated Tool for Improving Measurements of the Internet Topology

The classical approach for Internet topology measurement consists in distributively collecting as much data as possible and merging it into one single piece of topology on which are conducted subsequent analysis. Although this approach may seem reasonable, in most cases network measurements performed in this way suffer from some or all of the following limitations: they give only partial views of the networks under concern, these views may be intrinsically biased, and they contain erroneous data due to the measurement tools. Here we present a new tool, named UDP Ping, that relies on a very different approach for the measurement of the Internet topology. Its basic principle is to measure the interface of a given target directed toward a monitor which sends the measurement probe. We demonstrate how to use it to deploy real world-wide measurements that provide reliable (i.e. bias and error free) knowledge of the Internet topology, namely the degree distribution of routers in the core Internet in our example.