Table Of ContentIntelligent Systems Reference Library 85
Dariusz Król
Damien Fay
Bogdan Gabryś Editors
Propagation
Phenomena
in Real World
Networks
Intelligent Systems Reference Library
Volume 85
Series editors
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
Lakhmi C. Jain, University of Canberra, Canberra, Australia, and
University of South Australia, Adelaide, Australia
e-mail: [email protected]
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ó
Dariusz Kr l Damien Fay
(cid:129)
ś
Bogdan Gabry
Editors
Propagation Phenomena
in Real World Networks
123
Editors
Dariusz Król BogdanGabryś
Department of InformationSystems Computational IntelligenceResearch
Wrocław UniversityofTechnology Group,
Wrocław SmartTechnologyResearch Centre
Poland BournemouthUniversity
Poole
Damien Fay UK
School ofDesign,Engineering
andComputing
BournemouthUniversity
Fern Barrow Poole
UK
ISSN 1868-4394 ISSN 1868-4408 (electronic)
Intelligent SystemsReference Library
ISBN 978-3-319-15915-7 ISBN 978-3-319-15916-4 (eBook)
DOI 10.1007/978-3-319-15916-4
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Preface
Propagation phenomena have become a pervasive and significant feature of real
world networks. These interdisciplinary phenomena are influencing science, engi-
neering, finance, business, and ultimately society itself. The development of
propagation techniques plays an important role in maintaining existing networks
andhasallowed,forexample,synchronizationinelectricalpowergrids,prediction
of complex system behavior, resource discovery and monitoring, locating biolog-
ical invasions and assessing damage, virus propagation control and containment,
and decomposition and immunization of social and large-scale infrastructure net-
works. By studying propagation processes, one can better understand information
andknowledgespreadinginsystemswhichinturncanleadtosomeimprovements
in performance and robustness.
The purpose of this book is to bring into one volume the different types of
propagation models and techniques including: epidemic models, models for trust
inference, coverage strategies for networks, vehicle flow propagation, bio-inspired
routing algorithms, P2P botnet attacks and defenses, fault propagation in gene-
cellular networks, malware propagation for mobiles, information propagation in
crisis situations, financial contagion in interbank networks, and finally how to
maximizethespreadofinfluenceinsocialnetworks.Thisvolumeprovidesaunique
compendiumofcurrentandemergingproblemsofpropagationandrelatedsubjects.
Thus, it is truly a guide designed for interdisciplinary use.
Fourteen chapters provided by established scientists in the area of complex
networks have been carefully selected to reflect the diversity, complexity, and the
depth and breadth of this multidisciplinary area which encompasses closely inter-
twined conceptual and empirical issues in real world networks.
More precisely, we start with the critical issues regarding epidemics and their
outburstsinnetworkstructures.Chapter1mainlyfocusedonsusceptible-infectious-
recovered (SIR) and susceptible-infectious-susceptible (SIS) models as well as the
various modeling techniques for studying cascading failures, where the damage
spreads through the network. Chapter 2 explores an interesting example of infor-
mation propagation provided by the shoaling and schooling behaviors offish. The
presentedfishalgorithm(FA),consideredasaswarmoptimization,usingbottom-up
v
vi Preface
learning of individual perception and the propagation of social information in the
population, can usefully spread information amongst the agents. In Chap. 3, an
approach named Appleseed, which is based on mechanics taken from neuropsy-
chology,knownasspreadingactivationmodels,ispresented.Severalalgorithmsfor
inferring trust and distrust that go beyond network structure and demonstrate their
accuracy in real social networks are evaluated and discussed. Chapter 4 examines
how basic network properties can affect the propagation of ideas, beliefs, and
behaviors that shape mental models, showing how these different structures can
either promote or hinder the adaptation of mental models, and indicating what
strategies may be used for improving them. Using methods from statistical
mechanics,inChap.5,flooding-basedstrategiesareshowntodeveloprandomwalk
strategies to maximize the coverage in large-scale unstructured networks, which
takesintoaccounttheresourceconstraintsintheformofconsumedbandwidthand
latency time.
Modeling propagation in transport and communication networks is the central
topicofthenexttwocontributions.Thefirstofthesechapters,Chap.6utilizesthree
kinds of Petri nets: place/transitions Petri nets, timed Petri nets, and hybrid Petri
netstomodelandsimulateagentsofatransportnetwork.Asimilarlineofresearch
isexploredinChap.7butthefocusisontheroleofpropagationphenomenainbio-
inspired routing.
Another popular area where propagation methods are highly topical is building
reliable systems with respect to errors, faults, and failures. This is the focus of
Chap. 8, where the life cycle of P2P botnets is studied. This chapter provides
guidanceforsecurityprofessionalsonhowtoimplementtwomitigationtechniques
againstP2PbotnetsindexpoisoningdefenseandSybildefense,andonemonitoring
technique—passive monitoring—to achieve better performance. Network modifi-
cationswiththeaimofenhancingrobustnessagainsttargetedattacksisproposedin
Chap. 9. The outlined procedure optimized for the cost function of Integral Effi-
ciencycouldbeusedtogeneratehighlyrobustandefficientnetworks.Chapter10in
turn examines problems related to propagation phenomena in biological networks.
As an example, carcinogenesis and cancer progression are examined as processes
that propagate molecular failures. These processes are based on evolutionary and
spatial evolutionary games, which describe propagation phenomena in time and
space. A number of recently developed propagation models and algorithms that
couldbeutilizedtounderstandmobilephonedatasetsarepresentedinChap.11.It
discusses how to analyze the spreading dynamics of mobile phone malware using
SIR, SIS, and SIDR epidemiological models, how to identify fraudulent activity
overaphonenetwork,andhowtopredictchurnersinaphonenetwork.Chapter12
deals with structural information propagation in crisis situations referring to
disaster,emergency,orcatastrophemanagementlooselydefinedasallactionstaken
before, during, and after the event. It proposes a framework for detecting and
propagating valuable information to the public, and to the first responders in par-
ticular.Amodelofafinancialnetworkandthethreemethodsofthepropagationof
losses: the linear threshold algorithm, the graph-theoretic approach, and the ficti-
tiousdefaultalgorithm,usedtosimulatecontagioninsuchanetworkisproposedin
Preface vii
Chap. 13. The volume closes with Chap. 14. It presents the state of the art in the
areaofmaximizingthespreadofinfluenceinsocialnetworks,limitationsofusinga
static network representation, and recent trends that use temporal properties of
social networks as an alternative.
We are extremely grateful to the Intelligent Systems Reference Library by
Springerforhavinghostedthisthemebook.Specialthanksareduetoserieseditors:
Janusz Kacprzyk, and Lakhmi C. Jain. We would principally like to express our
mostsincerethanksandgreatappreciationtoallthosecolleagueswhohavehelped
us in the realization of this book, in particular, to the contributors and referees.
Many researchers have contributed to this volume with their work. We deeply
thank them for their great contributions. In alphabetical order they are: Baber
Aslam, Damian Borys, Abdelhamid Bouchachia, Anthony Brabazon, Newton
Paulo Bueno, František Čapkovič, Wei Cui, Fabio Daolio, Derek Doran, Mario
Eboli, Niloy Ganguly, Jennifer Golbeck, Hans J. Herrmann, Roman Jaksik, Prze-
mysław Kazienko, Pavel Krömer, Michał Krześlak, Vitor H.P. Louzada, Veena
Mendiratta, Radosław Michalski, Petr Musilek, Subrata Nandi, Michael ONeill,
Daniela Pohl, Jarosław Śmieja, Andrzej Świerniak, Somnath Tagore, Marco
Tomassini, Ping Wang, Lei Wu, Cai-Nicolas Ziegler, and Cliff C. Zou.
Warm thanks are also due to the following referees who reviewed the chapters
with remarkable expertise and engagement: Nuno Araujo, Francesca Arcelli
Fontana, Emili Balaguer-Ballester, František Čapkovič, Richard Clegg, Ireneusz
Czarnowski, Anirban Dasgupta, Paul Davidsson, Damien Fay, Evelina Gabasova,
BogdanGabryś,SergioGomez,HamedHaddadi,JasonJung,DariuszKról,HuiLi,
Katarzyna Musial, Marco Rossetti, Ruben Sanchez-Garcia, Antonio Scala, Fabio
Stella, Mirko Viroli, Ye Wu, and Rong Yang.
By way ofconclusion, it isworthemphasizingthat this work was supported by
the project To what extent can design principles for complex networks be derived
fromthestudyoferrorpropagationphenomenoninsmartandbio-inspirednetwork
structures? within the 7th European Community Framework Programme under
grant FP7-PEOPLE-2010-IEF-274375-EPP. Indeed, the true situation is barely
captured by saying that without the EC project it would not have been possible to
start, and no less complete, this volume.
We anticipate that our work results in a coherent and comprehensive presenta-
tion of the vast recent research activity concerning propagation processes in real
world networks. The large number of citations that found room in every chapter
makes us believe that the present volume will be a convenient reference to all
scholars who consider studying this exciting research area. It should be also clear
that these 14 chapters should not be construed as covering all aspects of propa-
gation research.
Wrocław, November 2014 Dariusz Król
Bournemouth Damien Fay
Bogdan Gabryś
Contents
1 Epidemic Models: Their Spread, Analysis and Invasions
in Scale-Free Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Somnath Tagore
1.1 Scale-Free Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Power-Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Epidemics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Branching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Network Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.1 Concurrency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Propagation Phenomena in Real World Networks. . . . . . . . . . 11
1.5 Network Definition and Measurement . . . . . . . . . . . . . . . . . . 11
1.5.1 Data Collection Process. . . . . . . . . . . . . . . . . . . . . . 13
1.6 Robustness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7 Models of Infections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.7.1 Susceptible-Infected-Recovered (SIR) . . . . . . . . . . . . 16
1.7.2 Susceptible-Infected-Susceptible (SIS). . . . . . . . . . . . 18
1.8 Epidemic Invasions, Propagations and Outbursts. . . . . . . . . . . 19
1.9 Combat and Immunization. . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.9.1 Complex Topologies and Heterogeneous Structures. . . 20
1.9.2 Damage Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.9.3 Immunity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.10 Understanding Cascading Failures, Natural Disturbances . . . . . 23
1.11 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Information Propagation in a Social Network: The Case
of a Fish Schooling Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . 27
A. Brabazon, W. Cui and M. O’Neill
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.1 Fish Schooling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.2 How Do Fish Schools Make Decisions? . . . . . . . . . . 29
ix
x Contents
2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.1 Application of Fish School Algorithms . . . . . . . . . . . 30
2.2.2 Golden Shiner Fish. . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3 Fish Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.3.1 Prior Period Velocity. . . . . . . . . . . . . . . . . . . . . . . . 32
2.3.2 Distributed Perception Influence. . . . . . . . . . . . . . . . 33
2.3.3 Individual Perception Influence. . . . . . . . . . . . . . . . . 34
2.4 Experimental Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.1 Benchmark Functions . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.2 Experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4.3 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . 39
2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.5.1 Hypotheses Examined. . . . . . . . . . . . . . . . . . . . . . . 40
2.5.2 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . 41
2.5.3 Analysis of Components in FA. . . . . . . . . . . . . . . . . 46
2.5.4 Parameter Sensitivity Analysis . . . . . . . . . . . . . . . . . 47
2.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 Models for Trust Inference in Social Networks. . . . . . . . . . . . . . . 53
Cai-Nicolas Ziegler and Jennifer Golbeck
3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.1 Trust Representation and Model. . . . . . . . . . . . . . . . 55
3.1.2 Overview of Trust Metrics for Social Networks . . . . . 55
3.2 Design of Local Group Trust Metrics . . . . . . . . . . . . . . . . . . 57
3.2.1 Outline of Advogato Maxflow . . . . . . . . . . . . . . . . . 57
3.2.2 The Appleseed Trust Metric. . . . . . . . . . . . . . . . . . . 60
3.2.3 Comparison of Advogato and Appleseed. . . . . . . . . . 71
3.3 Distrust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.3.1 Semantics of Distrust . . . . . . . . . . . . . . . . . . . . . . . 73
3.3.2 Incorporating Distrust into Appleseed . . . . . . . . . . . . 75
3.4 Expanding Network Coverage . . . . . . . . . . . . . . . . . . . . . . . 79
3.4.1 Revisiting Trust Inference Algorithms. . . . . . . . . . . . 79
3.4.2 Experimental Analysis. . . . . . . . . . . . . . . . . . . . . . . 82
3.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.5 Discussion and Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4 Assessing the Role of Network Effects in Propagation
Phenomena in Real World Networks. . . . . . . . . . . . . . . . . . . . . . 91
Newton Paulo Bueno
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.2 Basic Concepts and Ideas on Networks . . . . . . . . . . . . . . . . . 92
4.2.1 Mean Degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
