SearcharxivSearch

arXiv subjects

Nikolaos Fountoulakis

Publications and source records attributed to Nikolaos Fountoulakis.

At least 19 recordsLinked to original sources

Majority dynamics on random graphs: the multiple states case

We study the evolution of majority dynamics with more than two states on the binomial random graph $G(n,p)$. In this process, each vertex has a state in $\{1,\ldots, k\}$, with $k\geq 3$, and at each round every vertex adopts state $i$ if it has more neighbours in state $i$ that in any other state. Ties are resolved randomly. We show that with high probability the process reaches unanimity in at most three rounds, if $np\gg n^{2/3}$.

math.PR

Dynamical Models for Random Simplicial Complexes

We study a general model of random dynamical simplicial complexes and derive a formula for the asymptotic degree distribution. This asymptotic formula encompasses results for a number of existing models, including random Apollonian networks and the weighted random recursive tree. It also confirms results on the scale-free nature of Complex Quantum Network Manifolds in dimensions $d > 2$, and special types of Network Geometry with Flavour models studied in the physics literature by Bianconi, Rahmede [$\mathit{Sci. Rep.} \; \mathbf{5},\text{ 13979 (2015) and }\mathit{Phys. Rev. E} \; \mathbf{93},\text{ 032315 (2016)}$].

math.PR

Percolation on random graphs with a fixed degree sequence

We consider bond percolation on random graphs with given degrees and bounded average degree. In particular, we consider the order of the largest component after the random deletion of the edges of such a random graph. We give a rough characterisation of those degree distributions for which bond percolation with high probability leaves a component of linear order, known usually as a giant component. We show that essentially the critical condition has to do with the tail of the degree distribution. Our proof makes use of recent technique introduced by Joos et al. [FOCS 2016, pp. 695--703], which is based on the switching method and avoids the use of the classic configuration model as well as the hypothesis of having a limiting object. Thus our results hold for sparse degree sequences without the usual restrictions that accompany the configuration model.

math.CO

The modularity of random graphs on the hyperbolic plane

Modularity is a quantity which has been introduced in the context of complex networks in order to quantify how close a network is to an ideal modular network in which the nodes form small interconnected communities that are joined together with relatively few edges. In this paper, we consider this quantity on a recent probabilistic model of complex networks introduced by Krioukov et al. (Phys. Rev. E 2010). This model views a complex network as an expression of hidden hierarchies, encapsulated by an underlying hyperbolic space. For certain parameters, this model was proved to have typical features that are observed in complex networks such as power law degree distribution, bounded average degree, clustering coefficient that is asymptotically bounded away from zero, and ultra-small typical distances. In the present work, we investigate its modularity and we show that, in this regime, it converges to 1 in probability.

math.PR

Condensation phenomena in preferential attachment trees with neighbourhood influence

We introduce a model of evolving preferential attachment trees where vertices are assigned weights, and the evolution of a vertex depends not only on its own weight, but also on the weights of its neighbours. We study the distribution of edges with endpoints having certain weights, and the distribution of degrees of vertices having a given weight. We show that the former exhibits a condensation phenomenon under a certain critical condition, whereas the latter converges almost surely to a distribution that resembles a power law distribution. Moreover, in the absence of condensation, we prove almost-sure setwise convergence of the related quantities. This generalises existing results on the Bianconi-Barabási tree as well as on an evolving tree model introduced by the second author.

math.PR

Limit theory of isolated and extreme points in hyperbolic random geometric graphs

Given $α\in (0, \infty)$ and $r \in (0, \infty)$, let ${\cal D}_{r, α}$ be the disc of radius $r$ in the hyperbolic plane having curvature $-α^2$. Consider the Poisson point process having uniform intensity density on ${\cal D}_{R, α}$, with $R = 2 \log(n/ ν),$ $n \in \mathbb{N}$, and $ν< n$ a fixed constant. The points are projected onto ${\cal D}_{R, 1}$, preserving polar coordinates, yielding a Poisson point process ${\cal P}_{α, n}$ on ${\cal D}_{R, 1}$. The hyperbolic geometric graph ${\cal G}_{α, n}$ on ${\cal P}_{α, n}$ puts an edge between pairs of points of ${\cal P}_{α, n}$ which are distant at most $R$. This model has been used to express fundamental features of complex networks in terms of an underlying hyperbolic geometry. For $α\in (1/2, \infty)$ we establish expectation and variance asymptotics as well as asymptotic normality for the number of isolated and extreme points in ${\cal G}_{α, n}$ as $n \to \infty$. The limit theory and renormalization for the number of isolated points are highly sensitive on the curvature parameter. In particular, for $α\in (1/2, 1)$, the variance is super-linear, for $α= 1$ the variance is linear with a logarithmic correction, whereas for $α\in (1, \infty)$ the variance is linear. The central limit theorem fails for $α\in (1/2, 1)$ but it holds for $α\in (1, \infty)$.

math.PR

Clustering in a hyperbolic model of complex networks

In this paper we consider the clustering coefficient and clustering function in a random graph model proposed by Krioukov et al.~in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are connected if they are at most a certain hyperbolic distance from each other. It has been shown that this model has various properties associated with complex networks, e.g. power-law degree distribution, short distances and non-vanishing clustering coefficient. Here we show that the clustering coefficient tends in probability to a constant $γ$ that we give explicitly as a closed form expression in terms of $α, ν$ and certain special functions. This improves earlier work by Gugelmann et al., who proved that the clustering coefficient remains bounded away from zero with high probability, but left open the issue of convergence to a limiting constant. Similarly, we are able to show that $c(k)$, the average clustering coefficient over all vertices of degree exactly $k$, tends in probability to a limit $γ(k)$ which we give explicitly as a closed form expression in terms of $α, ν$ and certain special functions. We are able to extend this last result also to sequences $(k_n)_n$ where $k_n$ grows as a function of $n$. Our results show that $γ(k)$ scales differently, as $k$ grows, for different ranges of $α$. More precisely, there exists constants $c_{α,ν}$ depending on $α$ and $ν$, such that as $k \to \infty$, $γ(k) \sim c_{α,ν} \cdot k^{2 - 4α}$ if $\frac{1}{2} < α< \frac{3}{4}$, $γ(k) \sim c_{α,ν} \cdot \log(k) \cdot k^{-1} $ if $α=\frac{3}{4}$ and $γ(k) \sim c_{α,ν} \cdot k^{-1}$ when $α> \frac{3}{4}$. These results contradict a claim of Krioukov et al., which stated that the limiting values $γ(k)$ should always scale with $k^{-1}$ as we let $k$ grow.

math.PR

Best response dynamics on random graphs

We consider evolutionary games on a population whose underlying topology of interactions is determined by a binomial random graph $G(n,p)$. Our focus is on 2-player symmetric games with 2 strategies played between the incident members of such a population. Players update their strategies synchronously. At each round, each player selects the strategy that is the best response to the current set of strategies its neighbours play. We show that such a system reduces to generalised majority and minority dynamics. We show rapid convergence to unanimity for $p$ in a range that depends on a certain characteristic of the payoff matrix. In the presence of a bias among the pure Nash equilibria of the game, we determine a sharp threshold on $p$ above which the largest connected component reaches unanimity with high probability. For $p$ below this critical value, where this does not happen, we identify those substructures inside the largest component that remain discordant throughout the evolution of the system.

math.CO

Resolution of a conjecture on majority dynamics: rapid stabilisation in dense random graphs

We study majority dynamics on the binomial random graph $G(n,p)$ with $p = d/n$ and $d > λn^{1/2}$, for some large $λ>0$. In this process, each vertex has a state in $\{-1,+1 \}$ and at each round every vertex adopts the state of the majority of its neighbours, retaining its state in the case of a tie. We show that with high probability the process reaches unanimity in at most four rounds. This confirms a conjecture of Benjamini, Chan, O' Donnel, Tamuz and Tan.

math.CO

Algebraic and combinatorial expansion in random simplicial complexes

In this paper we consider the expansion properties and the spectrum of the combinatorial Laplace operator of a $d$-dimensional Linial-Meshulam random simplicial complex, above the cohomological connectivity threshold. We consider the spectral gap of the Laplace operator and the Cheeger constant as this was introduced by Parzanchevski, Rosenthal and Tessler ($Combinatorica$ 36, 2016). We show that with high probability the spectral gap of the random simplicial complex as well as the Cheeger constant are both concentrated around the minimum co-degree of among all $d-1$-faces. Furthermore, we consider a generalisation of a random walk on such a complex and show that the associated conductance is with high probability bounded away from 0.

math.CO

High-dimensional bootstrap processes in evolving simplicial complexes

We study bootstrap percolation processes on random simplicial complexes of some fixed dimension $d \geq 3$. Starting from a single simplex of dimension $d$, we build our complex dynamically in the following fashion. We introduce new vertices one by one, all equipped with a random weight from a fixed distribution $μ$. The newly arriving vertex selects an existing $(d-1)$-dimensional face at random, with probability proportional to some positive and symmetric function $f$ of the weights of its vertices, and attaches to it by forming a $d$-dimensional simplex. After a complex on $n$ vertices is constructed, we infect every vertex independently at random with some probability $p = p(n)$. Then, in consecutive rounds, we infect every healthy vertex the neighbourhood of which contains at least $r$ disjoint $(k-1)$-dimensional, fully infected faces. Using a reduction to the generalised Pólya urn schemes, we determine the value of critical probability $p_c = p_c (n; μ, f)$, such that if $p \gg p_c$ then, with probability tending to 1 as $n \to \infty$, the infection spreads to the whole vertex set of the complex, while if $p \ll p_c$ then the infection process stops with healthy vertices remaining in the complex.

math.PR

Hamilton cycles and perfect matchings in the KPKVB model

In this paper we consider the existence of Hamilton cycles and perfect matchings in a random graph model proposed by Krioukov et al.~in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are connected if they are at most a certain hyperbolic distance from each other. It has been previously shown that this model has various properties associated with complex networks, including a power-law degree distribution, "short distances" and a strictly positive clustering coefficient. The model is specified using three parameters: the number of nodes $n$, which we think of as going to infinity, and $α, ν> 0$, which we think of as constant. Roughly speaking $α$ controls the power law exponent of the degree sequence and $ν$ the average degree. Here we show that for every $α< 1/2$ and $ν=ν(α)$ sufficiently small, the model does not contain a perfect matching with high probability, whereas for every $α< 1/2$ and $ν=ν(α)$ sufficiently large, the model contains a Hamilton cycle with high probability.

math.PR

Typical distances in a geometric model for complex networks

We study typical distances in a geometric random graph on the hyperbolic plane. Introduced by Krioukov et al.~\cite{ar:Krioukov} as a model for complex networks, $N$ vertices are drawn randomly within a bounded subset of the hyperbolic plane and any two of them are joined if they are within a threshold hyperbolic distance. With appropriately chosen parameters, the random graph is sparse and exhibits power law degree distribution as well as local clustering. In this paper we show a further property: the distance between two uniformly chosen vertices that belong to the same component is doubly logarithmic in $N$, i.e., the graph is an ~\emph{ultra-small world}. More precisely, we show that the distance rescaled by $\log \log N$ converges in probability to a certain constant that depends on the exponent of the power law. The same constant emerges in an analogous setting with the well-known \emph{Chung-Lu} model for which the degree distribution has a power law tail.

math.CO

A phase transition regarding the evolution of bootstrap processes in inhomogeneous random graphs

A bootstrap percolation process on a graph with infection threshold $r\ge 1$ is a dissemination process that evolves in time steps. The process begins with a subset of infected vertices and in each subsequent step every uninfected vertex that has at least $r$ infected neighbours becomes infected and remains so forever. Critical phenomena in bootstrap percolation processes were originally observed by Aizenman and Lebowitz in the late 1980s as finite-volume phase transitions in $\mathbb{Z}^d$ that are caused by the accumulation of small local islands of infected vertices. They were also observed in the case of dense (homogeneous) random graphs by Janson, Łuczak, Turova and Valier (2012). In this paper, we consider the class of inhomogeneous random graphs known as the Chung-Lu model: each vertex is equipped with a positive weight and each pair of vertices appears as an edge with probability proportional to the product of the weights. In particular, we focus on the sparse regime, where the number of edges is proportional to the number of vertices. The main results of this paper determine those weight sequences for which a critical phenomenon occurs: there is a critical density of vertices that are infected at the beginning of the process, above which a small (sublinear) set of infected vertices creates an avalanche of infections that in turn leads to an outbreak. We show that this occurs essentially only when the tail of the weight distribution dominates a power law with exponent 3 and we determine the critical density in this case.

math.PR

Law of large numbers for the largest component in a hyperbolic model of complex networks

We consider the component structure of a recent model of random graphs on the hyperbolic plane that was introduced by Krioukov et al. The model exhibits a power law degree sequence, small distances and clustering, features that are associated with the so-called complex networks. The model is controlled by two parameters $α$ and $ν$ where, roughly speaking, $α$ controls the exponent of the power law and $ν$ controls the average degree. Refining earlier results, we are able to show a law of large numbers for the largest component. That is, we show that the fraction of points in the largest component tends in probability to a constant $c$ that depends only on $α,ν$, while all other components are sublinear. We also study how $c$ depends on $α, ν$. To deduce our results, we introduce a local approximation of the random graph by a continuum percolation model on $\mathbb{R}^2$ that may be of independent interest.

math.PR

Bootstrap percolation and the geometry of complex networks

On a geometric model for complex networks (introduced by Krioukov et al.) we investigate the bootstrap percolation process. This model consists of random geometric graphs on the hyperbolic plane having $N$ vertices, a dependent version of the Chung-Lu model. The process starts with infection rate $p=p(N)$. Each uninfected vertex with at least $\mathbf{r}\geq 1$ infected neighbors becomes infected, remaining so forever. We identify a function $p_c(N)=o(1)$ such that a.a.s.\ when $p\gg p_c(N)$ the infection spreads to a positive fraction of vertices, whereas when $p\ll p_c(N)$ the process cannot evolve. Moreover, this behavior is "robust" under random deletions of edges.

math.PR

Local majority dynamics on preferential attachment graphs

Suppose in a graph $G$ vertices can be either red or blue. Let $k$ be odd. At each time step, each vertex $v$ in $G$ polls $k$ random neighbours and takes the majority colour. If it doesn't have $k$ neighbours, it simply polls all of them, or all less one if the degree of $v$ is even. We study this protocol on the preferential attachment model of Albert and Barabási, which gives rise to a degree distribution that has roughly power-law $P(x) \sim \frac{1}{x^{3}}$, as well as generalisations which give exponents larger than $3$. The setting is as follows: Initially each vertex of $G$ is red independently with probability $α< \frac{1}{2}$, and is otherwise blue. We show that if $α$ is sufficiently biased away from $\frac{1}{2}$, then with high probability, consensus is reached on the initial global majority within $O(\log_d \log_d t)$ steps. Here $t$ is the number of vertices and $d \geq 5$ is the minimum of $k$ and $m$ (or $m-1$ if $m$ is even), $m$ being the number of edges each new vertex adds in the preferential attachment generative process. Additionally, our analysis reduces the required bias of $α$ for graphs of a given degree sequence studied by the first author (which includes, e.g., random regular graphs).

math.PR

Clustering and the hyperbolic geometry of complex networks

Clustering is a fundamental property of complex networks and it is the mathematical expression of a ubiquitous phenomenon that arises in various types of self-organized networks such as biological networks, computer networks or social networks. In this paper, we consider what is called the global clustering coefficient of random graphs on the hyperbolic plane. This model of random graphs was proposed recently by Krioukov et al. as a mathematical model of complex networks, under the fundamental assumption that hyperbolic geometry underlies the structure of these networks. We give a rigorous analysis of clustering and characterize the global clustering coefficient in terms of the parameters of the model. We show how the global clustering coefficient can be tuned by these parameters and we give an explicit formula for this function.

math.PR