A Triclustering Approach for Time Evolving Graphs

This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make an a priori discretization. Experiments conducted on a synthetic dataset illustrate the good behaviour of the technique, and a study of a real-life dataset shows the potential of the proposed approach for exploratory data analysis

Survivability in Time-varying Networks

Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework to the robust design of a real-world bus communication network

Resource considerate data routing through satellite networks

In many envisaged satellite-based networks, such as constellations or federations, there often exists a desire to reduce data latency, increase delivered data volume, or simply exploit unused resources. A strategy is presented that achieves efficient routing of data, in a store-carry-forward fashion, through satellite networks that exhibit delay- and disruption-tolerant network characteristics. This network-layer protocol, termed Spae, exploits information about the schedule of future contacts between network nodes, because satellite motion is deterministic, along with the capacity of these contacts to route data in such a way as to avoid significant overcommitment of data along a resource limited journey. Results from simulations of a federated satellite system indicate consistent benefit in terms of network performance over other, less-sophisticated, conventional methods, and comparable performance to a packet-optimal, full-knowledge approach

Global Impacts Report 2017

Our fifth Global Impacts Report reflects on the progress of the MSC over the past 20 years, examines the sustainability performance of certified fisheries around the world and highlights areas of future interest

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Statistical mechanics of complex networks

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.Comment: 54 pages, submitted to Reviews of Modern Physic

Temporal connectivity of vehicular networks: the power of store-carry-and-forward

Proceeding of: 2015 IEEE Vehicular Networking Conference (VNC), Kyoto, Japan, 16-18 December, 2015Store-carry-and-forward is extensively used in vehicular environments for many and varied purposes, including routing, disseminating, downloading, uploading, or offloading delay-tolerant content. The performance gain of store-carry-and-forward over traditional connected forwarding is primarily determined by the fact that it grants a much improved network connectivity. Indeed, by letting vehicles physically carry data, store-carry-and-forward adds a temporal dimension to the (typically fragmented) instantaneous network topology that is employed by connected forwarding. Temporal connectivity has thus a important role in the operation of a wide range of vehicular network protocols. Still, our understanding of the dynamics of the temporal connectivity of vehicular networks is extremely limited. In this paper, we shed light on this underrated aspect of vehicular networking, by exploring a vast space of scenarios through an evolving graph-theoretical approach. Our results show that using store-carry-and-forward greatly increases connectivity, especially in very sparse networks. Moreover, using store-carry-and-forward mechanisms to share content within a geographically-bounded area can be very efficient, i.e., new entering vehicles can be reached rapidly.This work was done while Marco Gramaglia was at CNR-IEIIT. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n.630211 ReFleX. The work of Christian Glacet was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.Publicad

Experience versus Talent Shapes the Structure of the Web

We use sequential large-scale crawl data to empirically investigate and validate the dynamics that underlie the evolution of the structure of the web. We find that the overall structure of the web is defined by an intricate interplay between experience or entitlement of the pages (as measured by the number of inbound hyperlinks a page already has), inherent talent or fitness of the pages (as measured by the likelihood that someone visiting the page would give a hyperlink to it), and the continual high rates of birth and death of pages on the web. We find that the web is conservative in judging talent and the overall fitness distribution is exponential, showing low variability. The small variance in talent, however, is enough to lead to experience distributions with high variance: The preferential attachment mechanism amplifies these small biases and leads to heavy-tailed power-law (PL) inbound degree distributions over all pages, as well as over pages that are of the same age. The balancing act between experience and talent on the web allows newly introduced pages with novel and interesting content to grow quickly and surpass older pages. In this regard, it is much like what we observe in high-mobility and meritocratic societies: People with entitlement continue to have access to the best resources, but there is just enough screening for fitness that allows for talented winners to emerge and join the ranks of the leaders. Finally, we show that the fitness estimates have potential practical applications in ranking query results