Agglomerative Percolation in Two Dimensions

We study a process termed "agglomerative percolation" (AP) in two dimensions. Instead of adding sites or bonds at random, in AP randomly chosen clusters are linked to all their neighbors. As a result the growth process involves a diverging length scale near a critical point. Picking target clusters with probability proportional to their mass leads to a runaway compact cluster. Choosing all clusters equally leads to a continuous transition in a new universality class for the square lattice, while the transition on the triangular lattice has the same critical exponents as ordinary percolation.Comment: Paper and supplementary figures and discussio

Explosive Percolation is Continuous, but with Unusual Finite Size Behavior

We study four Achlioptas type processes with "explosive" percolation transitions. All transitions are clearly continuous, but their finite size scaling functions are not entire holomorphic. The distributions of the order parameter, the relative size $s_{\rm max}/N$ of the largest cluster, are double-humped. But -- in contrast to first order phase transitions -- the distance between the two peaks decreases with system size $N$ as $N^{-\eta}$ with $\eta > 0$. We find different positive values of $\beta$ (defined via $< s_{\rm max}/N > \sim (p-p_c)^\beta$ for infinite systems) for each model, showing that they are all in different universality classes. In contrast, the exponent $\Theta$ (defined such that observables are homogeneous functions of $(p-p_c)N^\Theta$) is close to -- or even equal to -- 1/2 for all models.Comment: 4 pages (including 4 figures), plus 7 pages of supplementary materia

Percolation Theory on Interdependent Networks Based on Epidemic Spreading

We consider percolation on interdependent locally treelike networks, recently introduced by Buldyrev et al., Nature 464, 1025 (2010), and demonstrate that the problem can be simplified conceptually by deleting all references to cascades of failures. Such cascades do exist, but their explicit treatment just complicates the theory -- which is a straightforward extension of the usual epidemic spreading theory on a single network. Our method has the added benefits that it is directly formulated in terms of an order parameter and its modular structure can be easily extended to other problems, e.g. to any number of interdependent networks, or to networks with dependency links.Comment: 6 pages, 5 figure

Irreversible Aggregation and Network Renormalization

Irreversible aggregation is revisited in view of recent work on renormalization of complex networks. Its scaling laws and phase transitions are related to percolation transitions seen in the latter. We illustrate our points by giving the complete solution for the probability to find any given state in an aggregation process $(k+1)X\to X$, given a fixed number of unit mass particles in the initial state. Exactly the same probability distributions and scaling are found in one dimensional systems (a trivial network) and well-mixed solutions. This reveals that scaling laws found in renormalization of complex networks do not prove that they are self-similar.Comment: 4 pages, 2 figure

Discontinuous Percolation Transitions in Epidemic Processes, Surface Depinning in Random Media and Hamiltonian Random Graphs

Discontinuous percolation transitions and the associated tricritical points are manifest in a wide range of both equilibrium and non-equilibrium cooperative phenomena. To demonstrate this, we present and relate the continuous and first order behaviors in two different classes of models: The first are generalized epidemic processes (GEP) that describe in their spatially embedded version - either on or off a regular lattice - compact or fractal cluster growth in random media at zero temperature. A random graph version of GEP is mapped onto a model previously proposed for complex social contagion. We compute detailed phase diagrams and compare our numerical results at the tricritical point in d = 3 with field theory predictions of Janssen et al. [Phys. Rev. E 70, 026114 (2004)]. The second class consists of exponential ("Hamiltonian", or formally equilibrium) random graph models and includes the Strauss and the 2-star model, where 'chemical potentials' control the densities of links, triangles or 2-stars. When the chemical potentials in either graph model are O(logN), the percolation transition can coincide with a first order phase transition in the density of links, making the former also discontinuous. Hysteresis loops can then be of mixed order, with second order behavior for decreasing link fugacity, and a jump (first order) when it increases

Random Sequential Renormalization of Networks I: Application to Critical Trees

We introduce the concept of Random Sequential Renormalization (RSR) for arbitrary networks. RSR is a graph renormalization procedure that locally aggregates nodes to produce a coarse grained network. It is analogous to the (quasi-)parallel renormalization schemes introduced by C. Song {\it et al.} (Nature {\bf 433}, 392 (2005)) and studied more recently by F. Radicchi {\it et al.} (Phys. Rev. Lett. {\bf 101}, 148701 (2008)), but much simpler and easier to implement. In this first paper we apply RSR to critical trees and derive analytical results consistent with numerical simulations. Critical trees exhibit three regimes in their evolution under RSR: (i) An initial regime $N_0^{\nu}\lesssim N<N_0$, where $N$ is the number of nodes at some step in the renormalization and $N_0$ is the initial size. RSR in this regime is described by a mean field theory and fluctuations from one realization to another are small. The exponent $\nu=1/2$ is derived using random walk arguments. The degree distribution becomes broader under successive renormalization -- reaching a power law, $p_k\sim 1/k^{\gamma}$ with $\gamma=2$ and a variance that diverges as $N_0^{1/2}$ at the end of this regime. Both of these results are derived based on a scaling theory. (ii) An intermediate regime for $N_0^{1/4}\lesssim N \lesssim N_0^{1/2}$, in which hubs develop, and fluctuations between different realizations of the RSR are large. Crossover functions exhibiting finite size scaling, in the critical region $N\sim N_0^{1/2} \to \infty$, connect the behaviors in the first two regimes. (iii) The last regime, for $1 \ll N\lesssim N_0^{1/4}$, is characterized by the appearance of star configurations with a central hub surrounded by many leaves. The distribution of sizes where stars first form is found numerically to be a power law up to a cutoff that scales as $N_0^{\nu_{star}}$ with $\nu_{star}\approx 1/4$

Percolation Properties of Complex Networks

This dissertation is devoted to the study of connectivity transitions in complex networks via classical and new percolation models. Networks of high complexity appear across many domains; from commerce, telecommunication, infrastructure, and society, to gene regulation, and even evolution. In many cases these networks exhibit a sudden emergence (or breakdown) of long-range connectivity as a result of local microscopic events; this is of particular importance since their proper functioning often relies crucially on connectivity. One of the well-developed theories that deals with the formation of connected clusters as a result of random microscopic interactions, is percolation theory. This theory has been frequently applied to the study of epidemics and connectivity in complex networks; however details of most spreading phenomena are more involved, and the minimal assumptions of ordinary percolation are not adequate to describe many of their features. Hence it is necessary to design generalized models of percolation to accommodate more layers of complexity in the study of epidemics and connectivity. In this thesis we try to develop and explore new models of percolation by relaxing the main two assumptions of ordinary percolation, namely independence and locality of interactions. One of the new models we propose is agglomerative percolation, where we let clusters grow along all their boundary instead of a single site. This modification in most cases leads to a novel type of percolation that is in a different universality class than the ordinary type. We study agglomerative transitions on several graphs to extract their scaling properties and critical exponents. We show that agglomerative percolation maps onto random sequential renormalization, a method we developed to study the renormalization group flow of networks, and argue that contrary to previous claims, at least some of the scaling observed in previous renormalization schemes is due to agglomerative percolation rather than an underlying fractality in the structure of networks. In a new class of percolation models called explosive percolation, we show that the sharp transitions observed in numerical data is an artifact of the finite system sizes in computer simulations, and these transitions are actually continuous. We also contribute to the ongoing challenges in the study of percolation properties of interdependent networks by developing an analytical framework based on epidemic spreading. Finally, we develop cooperative percolation which can be applied to diverse settings, and show that adding cooperative effects to percolation models can change percolation properties dramatically; thus cooperativity --- which is in fact present in many social and physical phenomena --- needs to be considered in modeling these systems