adam w. hackett CASCADEDYNAMICSON COMPLEXNETWORKS CASCADEDYNAMICSON COMPLEXNETWORKS adam w. hackett A thesis submitted for the degree of Doctor of Philosophy in the Department of Mathematics and Statistics Faculty of Science and Engineering University of Limerick supervisor: Prof. James P. Gleeson head of department: Dr. Mark Burke October 2011 Cascade Dynamics on Complex Networks, A thesis submitted for the degree of Doctor of Philosophy, © Adam W. Hackett 2011 To my parents. The mind is not a vessel to be filled, but a fire to be kindled. — after Plutarch[ 78] CASCADEDYNAMICSONCOMPLEXNETWORKS adam w. hackett ABSTRACT The network topologies on which many natural and synthetic systems are built provide ideal settings for the emergence of complex phenomena. One well-studied manifestation of this, called a cascade or avalanche, is observed when interactions between the components of a system allow an initially localized effect to propagate globally. For example, the malfunction of technological systems like email networks or electrical power grids is often attributable to a cascade of failures triggered by some isolated event. Similarly, the transmission of infectious diseases and the adoption of innovations or cultural fads may induce cascades among people in society. In recent years, it has been extensively demonstrated that the dynamics of cascades depends sensitively on the patterns of interaction laid out in the underlying network. One of the goals of network theory is to provide a solid theoretical basis for this dependence. In order to do this it is necessary, first, to construct network models that are both mathematically sound and capture the salient features of their real-world counterparts. So far, there has been limited success in this direction. The primary shortcoming of most existing network models in this regard is their lack of realistic structural motifs, in particular the absence of significant levels of clustering, which refers to the propensity of triples of connected vertices to form triangles, and is a prominent feature of networked systems across multiple settings. In this thesis we investigate the interplay between network structure and cascade dynamics. Beginning with dynamics, we consider an analytically tractable technique to determine the expected cascade size in a broad range of dynamical models on locally tree-like networks of arbitrary degree distribution. We validate this approach by demonstrating its excellent agreement with the results of extensive numerical simulations, and closely examine its applicability to real socio-technological systems. Here we focus particularly on problems relating to social influence and opinion formation, and we develop a number of important modifications of the basic theory. Following this, we turn our attention to the structural characterization of networks. We investigate the properties of a new generation of network models that incorporate clustering by embedding cliques of fully connected vertices within a locally tree-like topology, and that thus directly extend the classical configuration model construction. In one such model, devised by a member of our group, the sizes of these cliques may vary, allowing one to prescribe a clustering spectrum to match empirically measured values. Finally, we significantly extend the theory of dynamics on tree-like net- works to these new, more structurally realistic ones. From this we uncover answers to some important questions, which have earned considerable recent attention, concerning the effects of increased clustering on cascades. vii PUBLICATIONS Some of the ideas and results presented in this thesis have appeared previously in the following publications: [76] Gleeson, J. P., Melnik,S., and Hackett,A. How clustering affects the bond percolation threshold in complex networks. Phys. Rev. E 81, 066114 (2010). [84] Hackett,A., Gleeson, J. P., and Melnik,S. Site percolation in clustered random networks. Int. J. Comp. Syst. Sci. 1, 25-30 (2011). [85] Hackett,A., Melnik,S., and Gleeson, J. P. Cascades on a class of clustered random networks. Phys. Rev. E 83, 056107 (2011). [99] Melnik,S., Hackett,A., Porter, M. A., Mucha J. P., and Gleeson, J. P. The unreasonable effectiveness of tree-based theory for networks with clustering. Phys. Rev. E 83, 036112 (2011). Chapter 5 contains work from [83] Hackett,A., and Gleeson, J. P. Cascades on graphs with embedded cliques. In preparation. ix The increment of meaning corresponds to the increased perception of the connections and continuities of the activities in which we are engaged. — John Dewey[ 40] ACKNOWLEDGMENTS I would like to thank my supervisor Prof. James Gleeson for his patient support and guidance throughout the course of this project. His unyielding work ethic and enthusiasm for tackling important research challenges by forging new collaborations has been a great source of inspiration to me as an aspiring academic, and I am grateful to have conducted the work presented here as a member of his Stochastic Dynamics and Complex Systems (SDCS) research group. Special thanks also go to Dr. Sergey Melnik1 for his helpful advice, comments, and suggestions during the writing of this thesis and previous publications. I have benefited greatly from thought-provoking discussions with and encouragement from colleagues in the Department of Mathematics and Statistics at the University of Limerick, and it has been a pleasure to watch the university, and this department in particular, go from strength to strength during my time here. Thanks to the late Prof. Frank Hodnett, the founding head of the department, for teaching me calculus in my first year as an undergraduate. May he rest in peace. Finally, it has been a privilege for me to contribute to the tireless efforts of all those associated with the Mathematics Applications Consortium for Science and Industry (MACSI) to reinforce collaborative ties between the Irish mathematics research community and industrial partners, and, more broadly, to foster a nationwide educational culture in which the application of mathematical methods of problem solving is appreciated as an impera- tive and intensely creative endeavour, long may it continue to do so. This work was funded by Science Foundation Ireland under programmes 06/IN.1/I366 and MACSI 06/MI/005. 1 Member of SDCS. xi CONTENTS 1 introduction 1 1.1 Cascades and Complexity . 1 1.2 The Rise of Network Theory . 2 1.2.1 The Origin of Graphs . 3 1.2.2 From Order to Randomness . 5 1.2.3 Networks in the Real World . 6 1.3 Thesis Organization . 10 2 definitions and concepts 11 2.1 Some Network Terminology . 11 2.2 Network Models . 15 2.2.1 Poisson Random Graphs . 15 2.2.2 The Configuration Model . 17 2.2.3 Small-World Networks . 18 2.2.4 The Barabási-Albert Model . 20 2.3 Processes on Networks . 23 2.3.1 Failure and Resilience . 24 2.3.2 Epidemics and Rumours . 27 3 modelling cascades 31 3.1 A Tree-based Analytical Approach . 32 3.1.1 Theory Versus Simulations . 34 3.2 The Influentials Hypothesis . 37 3.2.1 Extension of Theory . 38 3.2.2 Approximation . 43 3.3 The Effectiveness of the Tree Analogy . 46 4 networks with clustering 53 4.1 A Gap in the Literature . 54 4.2 Two Novel Approaches . 56 4.2.1 Edge-Triangle Graphs . 56 4.2.2 Clique-based Graphs . 59 4.3 Comparison of Models . 62 5 cascades and clustering: a synthesis 67 5.1 Cascades on Edge-Triangle Graphs . 68 5.1.1 Cascade Propagation . 69 5.1.2 Response Functions . 73 5.1.3 The Effects of Clustering . 77 5.2 Cascades on Clique-based Graphs . 83 5.2.1 Cascade Propagation . 85 5.2.2 Active Clique Neighbours . 88 5.2.3 Response Functions . 95 5.3 Towards a Unified Framework . 98 6 summary and conclusions 103 a other aspects of influentials theory 111 a.1 Critical Seed Fraction . 111 a.2 Single Seed Adjustment . 115 b further details of a synthesis 119 xiii xiv contents b.1 Concerning Edge-Triangle Graphs . 119 b.1.1 On the Edge-Triangle Cascade Condition . 119 b.1.2 Counting Argument for the Effects of Clustering . 120 b.2 Concerning Clique-based Graphs . 121 b.2.1 On Active Clique Neighbours . 121 c some numerical algorithms 123 bibliography 127 LISTOFFIGURES Figure 1.1 The seven bridges of Königsberg . 4 Figure 2.1 A Poisson random graph . 16 Figure 2.2 A small-world network . 19 Figure 2.3 A scale-free network . 22 Figure 2.4 The birth of the giant connected component . 26 Figure 3.1 Tree-based theory for cascade dynamics . 33 Figure 3.2 Cascade dynamics of Watts’s model on Poisson ran- dom graphs: uniform threshold distribution . 35 Figure 3.3 Cascade dynamics of Watts’s model on Poisson ran- dom graphs: Gaussian threshold distribution . 36 Figure 3.4 Cascade dynamics of Watts’s model on Poisson ran- dom graphs: uniform threshold distribution; average seed, and influential seed . 41 Figure 3.5 Cascade dynamics of Watts’s model on scale-free net- works: uniform threshold distribution; average seed, and influential seed . 42 Figure 3.6 Cascade dynamics of Watts’s model on Poisson ran- dom graphs: uniform threshold distribution; average seed, and approximation of influential seed . 44 Figure 3.7 Cascade dynamics of Watts’s model on scale-fee net- works: uniform threshold distribution; average seed, and approximation of influential seed . 45 Figure 3.8 Bond percolation on two real-world networks . 50 Figure 4.1 Local topology in an edge-triangle graph . 57 Figure 4.2 Local topology in a graph with clique-based clustering 60 Figure 4.3 Bond percolation threshold as a function of clustering in three unique classes of random regular graphs . 64 Figure 5.1 Level-by-level cascade propagation in a ps,t graph . 70 Figure 5.2 Site percolation on ps,t graphs: Poisson degree distri- bution; minimum clustering and maximum clustering 79 Figure 5.3 The effects of clustering in site, and bond percolation, and Watts’s model on z-regular ps,t graphs .