DOCSLIB.ORG

Complex Networks 2018 the 7Th International Conference on Complex Networks and Their Applications

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Complex Networks 2018 the 7Th International Conference on Complex Networks and Their Applications 1 COMPLEX NETWORKS 2018 THE 7TH INTERNATIONAL CONFERENCE ON COMPLEX NETWORKS AND THEIR APPLICATIONS December 11 - 13, 2018 Cambridge, United Kingdom Dear Colleagues and Friends, It is a great pleasure to welcome you in Cambridge for the 7th edition of the International Conference on Complex Networks and Their Applications. Cambridge is an elegant city very well connected with the world, and it is a very inspiring place. Its University dates from 1231, i.e. it is the second-oldest university in the English-speaking world and the world's fourth-oldest surviving university. The University of Cambridge is consistently ranked among the foremost universities in the world. 118 Nobel Laureates, 11 Fields Medalists, 6 Turing Award winners and 15 British Prime Ministers have been affiliated with Cambridge as students, alumni, faculty or research staff. The Colleges also represent the lifestyle, some of them contain important piece of arts and provide the visitor with the opportunity of a truly interdisciplinary and multidisciplinary experience. The conference is hosted in the Department of Computer Science and Technology. It was founded in 1937 (as the Mathematical Laboratory) by John Edward Lennard- Jones who was well known among scientists for his work on intermolecular forces. The department led original pioneering works in building complete computers (the EDSAC was commissioned in 1949 and the EDSAC 2 in 1958). Former members of the department have introduced in Computer Science the first videogame, the ancestor of the ASCII code, the concept of subroutine, the pi calculus and several programming languages such as C++, raspberry pi and others. The Cambridge Diploma in Computer Science was the world's first taught course in computing, starting in 1953. We wish you a very productive conference. We are confident that you will benefit from its rich program with stimulating discussions and many opportunities of networking. Through our social events, you will also discover some aspects of Cambridge's culture. Our wish is that you will enjoy this conference, contribute effectively toward it and take back with you knowledge, experiences, contacts and happy memories of this 7th edition of the International Conference on Complex Networks and Their Applications. Welcome to Cambridge! Pietro Liò Luca Maria Aiello Jon Crowcroft Renaud Lambiotte University of Cambridge Nokia Bell Labs University of Cambridge University of Oxford 1 TABLE OF CONTENTS Conference Events 5 Tutorial: Jesús Gómez-Gardeñes – Network Epidemiology: From simple to 6 data-driven models Tutorial: Silvio Lattanzi – From micro to macro: ego-network analysis and 7 its applications Keynote Day 1: Aristides Gionis – Maximizing diversity in social networks 8 [Elsevier Online Social Networks and Media Lecture] Keynote Day 1: Vittoria Colizza – Vulnerability of networked host 9 populations to epidemics [Springer Applied Network Science Lecture] Keynote Day 1: Romualdo Pastor-Satorras – Effects of Social Influence on 10 Collective Motion Keynote Day 2: Heather Harrington - Topological data analysis for 11 investigation of dynamics and biological networks Keynote Day 2: Hernan Makse - Essential nodes and keystone species in 12 the brain, ecosystems and social systems [PLOS Lecture] Keynote Day 2: Markus Strohmaier - Network analysis literacy: a 13 socioinformatic approach Keynote Day 3: Donald Towsley - Motifs in Social Networks 14 [MDPI Future Internet Lecture] Keynote Day 3: Sune Lehmann - Measuring Social Networks with High 15 Resolution: What have we learned? Sessions Day 1 16 Lighting L1: Networks in Finance and Economics – Structural Network 18 Measures Poster P1: Biological Networks - Community Structure - Link Analysis and 19 Ranking Oral O1A: Diffusion and Epidemics 20 2 Oral O1B: Quantifying Success 20 Oral O1C: Network Neuroscience 21 Oral O2A: Link Analysis and Ranking 21 Oral O2B: Resilience and Control 22 Oral O2C: Ecological Networks and Food Webs 22 Poster P2: Diffusion and Epidemics - Modeling Human Behavior - Machine 23 Learning and Networks Oral O3A: Network Models 24 Oral O3B: Multilayer Networks 24 Oral O3C: Social Networks 25 Sessions Day 2 26 Lighting L2: Social Networks – Diffusion, Resilience and Control 28 Poster P3: Dynamics of/on Networks - Multilayer Networks - Network 29 Neuroscience Oral O4A: Network Models 30 Oral O4B: Machine Learning and Networks 30 Oral O4C: Networks in Finance and Economics 31 Oral O5A: Diffusion and Epidemics 31 Oral O5B: Community Structure 32 Oral O5C: Modeling Human Behavior 32 Poster P4: Network Analysis - Resilience and Control - Urban Systems and 33 Networks Oral O6A: Structural Network Measures 34 Oral O6B: Urban Systems and Networks 34 Oral O6C: Resilience and Control 35 Sessions Day 3 36 Lighting L3: Machine Learning and Networks - Network models 37 Poster P5: Network Models - Networks in Finance and Economics - Social 37 Networks 3 Oral O7A: Diffusion and Epidemics 39 Oral O7B: Community Structure 39 Oral O7C: Biological Networks 40 Oral O8A: Modeling Human Behavior 40 Oral O8B: Resilience and Control 41 Oral O8C: Link Analysis and Ranking 41 Oral O9A: Dynamics on/of Networks 42 Oral O9B: Social Networks 42 Oral O9C: Network Analysis 43 Social Events Lunch 44 Welcome Reception Day 1 45 Dinner Banquet Day 2 46 Program at a Glance all days 48 4 CONFERENCE EVENTS Monday, December 10th, 2010 13:00 – 15:30 Tutorial 1: Jesús Gómez-Gardeñes 16:00 – 18:30 Tutorial 2: Silvio Lattanzi Tuesday, December 11th, 2018 08:45 – 09:00 Opening 09:00 – 09:35 Keynote Speaker: Aristides Gionis 10:30 – 11:05 Keynote Speaker: Vittoria Colizza 16:55 – 17:30 Keynote Speaker: Romualdo Pastor-Satorras 20:00 – 22:00 Welcome Reception Wednesday, December 12th, 2018 08:45 – 09:20 Keynote Speaker: Heather Harrington 10:15 – 10:50 Keynote Speaker: Hernan Makse 16:55 – 17:30 Keynote Speaker: Markus Strohmaier 20:00 – 22:00 Dinner Banquet Thursday, December 13th, 2018 08:45 – 09:20 Keynote Speaker: Donald Towsley 16:25 – 17:00 Keynote Speaker: Sune Lehmann 18:30 – 18:45 Closing Ceremony 5 MONDAY, DECEMBER 10th, 2018 Tutorials Jesús GÓMEZ-GARDEÑES University of Saragoza, Spain Jesus Gomez-Gardeñes is Associate Professor and head of the Group of Theoretical and Applied Modeling (GOTHAM) at the Institute of Biocomputation and Physics of Complex Systems (BIFI) of the University of Zaragoza (Spain). His main fields of research are statistical physics, nonlinear dynamics and the theory of complex networks. Within these disciplines he has mainly focused in the study of the emergence of collective phenomena out of nonlinearity and the structure of interactions in complex systems. Along these lines he has studied some paradigmatic problems such as energy localization, synchronization, random walks, traffic congestion, disease propagation and evolutionary dynamics. He has authored more than 100 scientific articles in international journals, including Nature Physics, PNAS, Physical Review Letters, Physics Reports, Science Advances, Nature Human Behavior among others. In the recent years he has focused on the study of multilayer networks and network epidemiology. Network Epidemiology: From simple to data-driven models In this tutorial we will deal with a topic that has advanced enormously in the recent decades thanks to contribution of network science: the modeling of epidemics. We will begin by reviewing the building blocks of the broad field of theoretical epidemiology: the compartmental models. From this point, we will progressively add ingredients aimed at capturing the real patterns of connectivity (networks) and mobility (metapopulations) observed in real societies. Finally, after analyzing the behavior of these models from the theoretical point of view, we will address the current challenges of epidemics prediction and the design of containment strategies. Chair: Sarah Morgan 6 Silvio LATTANZI Google Research Europe, Switzerland Silvio Lattanzi is a Research Scientist at Google Research Europe since April 2017. Before he was in the NY Algorithm group at Google New York from January 2011 to March 2017. He received my PhD from Sapienza University of Rome under the supervision of Alessandro Panconesi. During his PhD he interned twice at Google and once at Yahoo! Research. His research interests are in the areas of algorithms, machine learning and information retrieval. From micro to macro: ego-network analysis and its applications Detecting the clustering structure of real-world networks has emerged as an important primitive in a wide range of data analysis tasks such as community detection, event detection, spam detection, computational biology, link prediction and many others. As a result, the study of the topology of real world networks and of their clustering (or community) structure is central in modern network analysis. In particular, in recent years, several models have been introduced to capture the community structure of social networks and numerous empirical studies analyzed the community structures at a macroscopic and microscopic levels. One of the main observations in this line of work is the lack of a clear macroscopic community structure in real world networks. In sharp contrast with these findings, it has been observed that while the community detection problem is hard at a macroscopic level, it becomes simple at a microscopic level. This is especially true when we restrict our attention to local structures know as ego-nets (a.k.a. ego-networks) which consist of the subgraph induced over the neighborhood of a single node in the graph. Intuitively,
Load more

Recommended publications
  • Defining and Identifying Cograph Communities in Complex Networks
    Home Search Collections Journals About Contact us My IOPscience Defining and identifying cograph communities in complex networks This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 New J. Phys. 17 013044 (http://iopscience.iop.org/1367-2630/17/1/013044) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 136.186.1.81 This content was downloaded on 18/05/2015 at 13:56 Please note that terms and conditions apply. New J. Phys. 17 (2015) 013044 doi:10.1088/1367-2630/17/1/013044 PAPER Defining and identifying cograph communities in complex networks OPEN ACCESS Songwei Jia1, Lin Gao1, Yong Gao2, James Nastos2, Yijie Wang3, Xindong Zhang1 and Haiyang Wang1 RECEIVED 1 School of Computer Science and Technology, Xidian University, Xi’an 710071, People’s Republic of China 14 July 2014 2 Department of Computer Science, University of British Columbia Okanagan, Kelowna, British Columbia, V1V 1V7 Canada ACCEPTED FOR PUBLICATION 3 Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA 11 December 2014 PUBLISHED E-mail: [email protected] 20 January 2015 Keywords: complex networks, community dection, centrality Content from this work may be used under the terms of the Creative Abstract Commons Attribution 3.0 licence. Community or module detection is a fundamental problem in complex networks. Most of the tradi- Any further distribution of tional algorithms available focus only on vertices in a subgraph that are densely connected among this work must maintain attribution to the author themselves while being loosely connected to the vertices outside the subgraph, ignoring the topologi- (s) and the title of the cal structure of the community.
    [Show full text]
  • Controllability of Social Networks and the Strategic Use of Random Information
    Controllability of Social Networks and the Strategic Use of Random Information Marco Cremonini1, Francesca Casamassima2, 1 University of Milan, Italy 2 Sogetel, Italy * [email protected] Abstract This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network, and considers two well-known strategies for influencing social contexts. One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad-hoc metrics defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of arXiv:1807.07761v1 [cs.SI] 20 Jul 2018 agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills, supporting our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.
    [Show full text]
  • Node-Link Network Visualizations
    Watson Health Cognitive Visualization Lab Perspective on Network Visualization Cody Dunne November 5, 2015 [email protected] ibm.biz/cogvislab Perspectives on Network Visualization Agenda Introduction Node-Link Composite Alternate Coordinated Discussion Cody Dunne, PhD – Cognitive Visualization RSM Web: ibm.biz/codydunne Email: [email protected] My research focuses on information visualization, visual analytics, and cognitive computing. My goal is to design and build interactive visualization systems for users to effectively help them reach their goals. Epidemiology & network type group overlap dynamic networks overviews Specifically I want to make data discovery, cleaning, exploration, and sharing results as fluid and easy and accessible as possible. While at IBM I have been involved in many projects to this end collaborating with teams across the organization, with more coming! I received a PhD and M.S. in Computer Science under Ben Shneiderman at the University of Maryland Human news term computer network literature exploration Computer Interaction Lab, and a B.A. in Computer Science occurrence traffic and Mathematics from Cornell College. I contribute to NodeXL and have worked at Microsoft Research and NIST. Network Visualization in IBM To name only a few TED WDA-LS WDA WGA Alchemy Twitter i2 Designers IOC Epidemics Network Visualization in IBM Services exposing network data (many more coming) Node-Link Network Visualizations Introduction Node-Link Composite Alternate Coordinated Discussion Layout Algorithms Matter Immense variation in layout readability and speed Hachul & Jünger, 2006 Readability Metrics for Layouts Algorithm Understanding Talk from the Graph Summit 2015: ibm.biz/gs_rm_slides ibm.biz/gs_rm_video Dunne C, Ross SI, Shneiderman B and Martino M (2015), "Readability metric feedback for aiding node-link visualization designers", IBM Journal of Research and Development.
    [Show full text]
  • Mon Sujet De Thèse Complet
    THÈSE pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE ALPES Spécialité : Automatique Arrêté ministériel : 7 août 2006 Présentée par Nicolas MARTIN Thèse dirigée par Carlos CANUDAS-DE-WIT et codirigée par Paolo FRASCA préparée au sein du GIPSA-lab dans l’école doctorale Electronique Electrotechnique Automatique & Traitement du signal (EEATS) Network partitioning algorithms with scale-free objective Thèse soutenue publiquement le 19 Février 2020, devant le jury composé de: Myriam PREISSMANN Directrice de recherche, G-SCOP, Présidente du jury Pierre-Alexandre BLIMAN Directeur de recherche, INRIA, Rapporteur Christophe CRESPELLE Maitre de conférence, Université Claude Bernard Lyon 1, Rapporteur Jacquelien Scherpen Professor, University of Groningen, Examinatrice Paolo FRASCA Chargé de recherche, GIPSA-lab, Co-directeur de thèse UNIVERSITÉ DE GRENOBLE ALPES EEATS Electronique Electrotechnique Automatique & Traitement du signal THÈSE pour obtenir le titre de docteur en sciences de l’Université de Grenoble Alpes Mention : Automatique Présentée et soutenue par Nicolas MARTIN Network partitioning algorithms with scale-free objective Thèse dirigée par Carlos CANUDAS-DE-WIT GIPSA-lab soutenue le 19/02/2020 Jury : Rapporteurs : Pierre-Alexandre BLIMAN - Directeur de recherche, INRIA Christophe CRESPELLE - Maitre de conférence, Université Claude Bernard Lyon 1 Co-directeur : Paolo FRASCA - Chargé de recherche, GIPSA-lab Présidente : Myriam PREISSMANN - Directrice de recherche, G-SCOP Examinatrice : Jacquelien SCHERPEN - Professor, University of Groningen Résumé L’analyse, l’estimation et le contrôle de systèmes dynamiques évoluant sur des réseaux se confronte à des problèmes de complexité lorsque le système considéré devient trop grand. Pour faire face à ces problèmes d’échelles des méthodes de réduction de modèle sont utilisées.
    [Show full text]
  • Supplementary Information
    Controllability of Complex Networks: Supplementary Information Yang-Yu Liu, Jean-Jacques Slotine, and Albert-L´aszl´oBarab´asi SUPPLEMENTARY (Dated:INFORMATION April 1, 2011) doi:10.1038/nature10011 Contents I. Introduction 2 II. Previous Work and Relation of Controllability to Other Problems 2 A. Controllability of Undirected Network 2 B. Network Synchronizability 4 C. Congestion Control 4 III. Structural Control Theory 5 A. Why Linear Dynamics? 6 B. Simple Examples 8 C. Structural Controllability Theorem 10 D. Minimum Inputs Theorem 13 IV. Maximum Matching 16 A. Statistical physics description 16 B. Internal energy 17 1. r regular random digraph 21 − 2. Poisson-distributed digraph 23 3. Exponentially distributed digraph 24 4. Power-law distributed digraphs 25 5. Static model 27 6. Chung-Lu model 29 C. Entropy 32 V. Control Robustness 37 VI. Network Datasets 382 References 42 I. INTRODUCTION This Supplementary Information is organized as follows. In Sec. II, we clarifyWWW.NATURE.COM/NATURE the fundamental | 1 differences between our work and previous research on network controllability. In Sec. III, we give a short introduction to the structural control theory, which can be simply applied to directed networks. The reasons why we focus on linear dynamics are given in Sec. III A. Some simple examples in Sec. III B demonstrate the difference between controllability, structural controllability and strong structural controllability. The sufficient and necessary conditions for a linear system to be structurally controllable are given by Lin’s structural controllability theorem (SCT), which is discussed in Sec. III C. Based on SCT, we derived the minimum input theorem in Sec.
    [Show full text]
  • Network Science Degree Correlation
    7 ALBERT-LÁSZLÓ BARABÁSI NETWORK SCIENCE DEGREE CORRELATION ACKNOWLEDGEMENTS MÁRTON PÓSFAI ROBERTA SINATRA GABRIELE MUSELLA SARAH MORRISON MAURO MARTINO AMAL HUSSEINI NICOLE SAMAY PHILIPP HOEVEL INDEX Introduction 1 Assortativity and Disassortativity 2 Measuring Degree Correlations 3 4 Structural Cutoffs 5 Correlations in Real Networks Generating Correlated Networks 6 The Impact of Degree Correlations 7 Summary 8 Homework 9 ADVANCED TOPICS 7.A Degree Correlation Coefficient 10 ADVANCED TOPICS 7.B Structural Cutoffs 11 Bibliography 12 Figure 7.0 (cover image) TheyRule.net by Josh On Created by Josh On, a San Francisco-based de- signer, the interactive website TheyRule.net uses a network representation to illustrate the interlocking relationship of the US economic class. By mapping out the shared board mem- bership of the most powerful U.S. companies, it reveals the influential role of a small num- ber of individuals who sit on multiple boards. Since its release in 2001, the project is inter- changeably viewed as art or science. This book is licensed under a Creative Commons: CC BY-NC-SA 2.0. PDF V24 18.09.2014 SECTION 7.1 INTRODUCTION Angelina Jolie and Brad Pitt, Ben Affleck and Jennifer Garner, Harri- son Ford and Calista Flockhart, Michael Douglas and Catherine Zeta-Jones, Tom Cruise and Katie Holmes, Richard Gere and Cindy Crawford (Figure 7.1). An odd list, yet instantly recognizable to those immersed in the head- line-driven world of celebrity couples. They are Hollywood stars that are or were married. Their weddings (and breakups) has drawn countless hours of media coverage and sold millions of gossip magazines.
    [Show full text]
  • Controllability of Social Networks and the Strategic Use of Random Information
    Cremonini and Casamassima Comput Soc Netw (2017) 4:10 DOI 10.1186/s40649-017-0046-2 RESEARCH Open Access Controllability of social networks and the strategic use of random information Marco Cremonini1* and Francesca Casamassima2 *Correspondence: [email protected] Abstract 1 University of Milan, Via Background: This work is aimed at studying realistic social control strategies for social Bramante 65, Crema, CR, Italy Full list of author information networks based on the introduction of random information into the state of selected is available at the end of the driver agents. Deliberately exposing selected agents to random information is a tech- article nique already experimented in recommender systems or search engines, and repre- sents one of the few options for infuencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Methods: Our research is based on a model of knowledge difusion applied to a time- varying adaptive network and considers two well-known strategies for infuencing social contexts: One is the selection of few infuencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely infuencing users. The two approaches have been studied in terms of network and difusion efects. The network efect is analyzed through the changes induced on network average degree and clustering coefcient, while the difusion efect is based on two ad hoc metrics which are defned to measure the degree of knowledge difusion and skill level, as well as the polarization of agent interests.
    [Show full text]
  • Measuring the Robustness of Network Controllability
    Measuring the robustness of network controllability Ashish Dhiman Measuring the robustness of network controllability by Ashish Kumar Dhiman in partial fulfilment of the requirement for the degree of Master of Science in Electrical Engineering Track Telecommunications and Sensing Systems at the Delft University of Technology, to be defended publicly on Tuesday August 04, 2020 at 10:00 AM. Student number: 4949366 Project duration: November 12, 2019 – August 04, 2020 Thesis committee: Prof. dr. ir. Robert Kooij, TU Delft, supervisor Dr. Maksim Kitsak, TU Delft Dr. Johan Dubbeldam, TU Delft Peng Sun, TU Delft, daily supervisor An electronic version of this thesis is available at http://repository.tudelft.nl/. Preface With this thesis, “Measuring the robustness of network controllability”, I complete my Masters of Science degree in Electrical Engineering in the track of Telecommunications and Sensing Systems at the Delft University of Technology (TU Delft). This work was realized at the Network Architecture and Services (NAS) group, within the faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at TU Delft. First of all, I would like to express my sincere gratitude to my supervisor Robert Kooij for giving me the opportunity to carry out this master thesis at the NAS group. I am thankful to you for your invaluable guidance and support throughout the entire duration of this project. Moreover, your constructive feedback and help with the presentation and report writing guided me in significantly improving my work. I would like to thank Peng Sun, my daily supervisor for his kind help whenever I needed and for the valuable brainstorming sessions.
    [Show full text]
  • Benchmarking Measures of Network Controllability on Canonical Graph Models
    J Nonlinear Sci https://doi.org/10.1007/s00332-018-9448-z Benchmarking Measures of Network Controllability on Canonical Graph Models Elena Wu-Yan1,2 · Richard F. Betzel2 · Evelyn Tang2 · Shi Gu2 · Fabio Pasqualetti3 · Danielle S. Bassett2,4,5,6 Received: 21 September 2017 / Accepted: 5 February 2018 © The Author(s) 2018. This article is an open access publication Abstract The control of networked dynamical systems opens the possibility for new discoveries and therapies in systems biology and neuroscience. Recent theoretical advances provide candidate mechanisms by which a system can be driven from one pre-specified state to another, and computational approaches provide tools to test those mechanisms in real-world systems. Despite already having been applied to study network systems in biology and neuroscience, the practical performance of these tools and associated measures on simple networks with pre-specified structure has yet to be assessed. Here, we study the behavior of four control metrics (global, average, modal, and boundary controllability) on eight canonical graphs (including Erd˝os–Rényi, regular, small-world, random geometric, Barábasi–Albert preferential attachment, and several modular networks) with different edge weighting schemes Communicated by Paul Newton. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00332- 018-9448-z) contains supplementary material, which is available to authorized users. B Danielle S. Bassett [email protected] 1 Department of Computer and Information
    [Show full text]
  • Petter Holme (2015), Modern Temporal Network Theory: a Colloquium
    Eur. Phys. J. B (2015) 88: 234 DOI: 10.1140/epjb/e2015-60657-4 THE EUROPEAN PHYSICAL JOURNAL B Colloquium Modern temporal network theory: a colloquium Petter Holmea Department of Energy Science, Sungkyunkwan University, 440-746 Suwon, Korea Received 10 August 2015 / Received in final form 16 August 2015 Published online 21 September 2015 – c EDP Sciences, Societ`a Italiana di Fisica, Springer-Verlag 2015 Abstract. The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions. 1 Introduction oneered temporal network theory. Still today, researchers rediscover the ideas Leslie Lamport and others used in the To understand how large connected systems work, one 1970’s to build a theory of distributed computing [4]. needs to zoom out and view them from a distance.
    [Show full text]
  • Robustness of Real Network Controllability to Degree Based
    ROBUSTNESS OF REAL NETWORK CONTROLLABILITY TO DEGREE BASED ATTACKS by David M. J. Lanigan APPROVED BY SUPERVISORY COMMITTEE: ___________________________________________ Dr. Justin Ruths, Chair ___________________________________________ Dr. Alvaro Cárdenas ___________________________________________ Dr. Tyler Summers Copyright 2018 David M. J. Lanigan All Rights Reserved To Mrs. Kurtz and Mrs. Kane. ROBUSTNESS OF REAL NETWORK CONTROLLABILITY TO DEGREE BASED ATTACKS by DAVID M. J. LANIGAN, BS THESIS Presented to the Faculty of The University of Texas at Dallas in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN MECHANICAL ENGINEERING THE UNIVERSITY OF TEXAS AT DALLAS May 2018 ACKNOWLEDGMENTS I would like to thank my supervising professor, Dr. Justin Ruths, for his guidance and advice throughout this project, as well as for giving me the opportunity to study and learn about graph theory and network control theory and its applications. Additionally, I would like to thank Dr. Tyler Summers and Dr. Alvaro Cardenas for their assistance in completing this project, and for their valuable input into its content. May 2018 v ROBUSTNESS OF REAL NETWORK CONTROLLABILITY TO DEGREE BASED ATTACKS David M. J. Lanigan, MS The University of Texas at Dallas, 2018 ABSTRACT Supervising Professor: Dr. Justin Ruths Real world complex networks vary greatly topologically from each other as well as from generated synthetic random networks. For example, social and biological networks typically have a community structure, while random networks do not. In order to investigate the robustness of the controllability of real networks to attacks on its edges, five different attacks based on the degree of the nodes were levied at common real-world networks systematically by removing either 2% or 5% of the edges, in steps, until 90% were removed.
    [Show full text]
  • Social Network Dynamics
    Universita` degli Studi di Pisa Dipartimento di Informatica Dottorato di Ricerca in Informatica Settore Scientifico Disciplinare: INF/01 Ph.D. Thesis Social Network Dynamics Giulio Rossetti Supervisor Supervisor Dino Pedreschi Fosca Giannotti University of Pisa ISTI-CNR May 5, 2015 Abstract This thesis focuses on the analysis of structural and topological network problems. In particular, in this work the privileged subjects of investigation will be both static and dynamic social networks. Nowadays, the constantly growing availability of Big Data describing human behaviors (i.e., the ones provided by online social networks, telco companies, insurances, airline companies. ) of- fers the chance to evaluate and validate, on large scale realities, the performances of algorithmic approaches and the soundness of sociological theories. In this scenario, exploiting data-driven methodologies enables for a more careful modeling and thorough understanding of observed phe- nomena. In the last decade, graph theory has lived a second youth: the scientific community has extensively adopted, and sharpened, its tools to shape the so called Network Science. Within this highly active field of research, it is recently emerged the need to extend classic network analytical methodologies in order to cope with a very important, previously underestimated, semantic infor- mation: time. Such awareness has been the linchpin for recent works that have started to redefine form scratch well known network problems in order to better understand the evolving nature of human interactions. Indeed, social networks are highly dynamic realities: nodes and edges appear and disappear as time goes by describing the natural lives of social ties: for this reason. it is mandatory to assess the impact that time-aware approaches have on the solution of network prob- lems.
    [Show full text]