Thresholds for Extreme Orientability

Multiple-choice load balancing has been a topic of intense study since the seminal paper of Azar, Broder, Karlin, and Upfal. Questions in this area can be phrased in terms of orientations of a graph, or more generally a k-uniform random hypergraph. A (d,b)-orientation is an assignment of each edge to d of its vertices, such that no vertex has more than b edges assigned to it. Conditions for the existence of such orientations have been completely documented except for the "extreme" case of (k-1,1)-orientations. We consider this remaining case, and establish: - The density threshold below which an orientation exists with high probability, and above which it does not exist with high probability. - An algorithm for finding an orientation that runs in linear time with high probability, with explicit polynomial bounds on the failure probability. Previously, the only known algorithms for constructing (k-1,1)-orientations worked for k<=3, and were only shown to have expected linear running time.Comment: Corrected description of relationship to the work of LeLarg

Combinatorics and Probability

The main theme of this workshop was the use of probabilistic methods in combinatorics and theoretical computer science. Although these methods have been around for decades, they are being refined all the time: they are getting more and more sophisticated and powerful. Another theme was the study of random combinatorial structures, either for their own sake, or to tackle extremal questions. Both themes were richly represented at the workshop, with many recent exciting results presented by the lecturers

A phase transition in block-weighted random maps

We consider the model of random planar maps of size $n$ biased by a weight $u>0$ per $2$-connected block, and the closely related model of random planar quadrangulations of size $n$ biased by a weight $u>0$ per simple component. We exhibit a phase transition at the critical value $u_C=9/5$. If $u<u_C$, a condensation phenomenon occurs: the largest block is of size $\Theta(n)$. Moreover, for quadrangulations we show that the diameter is of order $n^{1/4}$, and the scaling limit is the Brownian sphere. When $u > u_C$, the largest block is of size $\Theta(\log(n))$, the scaling order for distances is $n^{1/2}$, and the scaling limit is the Brownian tree. Finally, for $u=u_C$, the largest block is of size $\Theta(n^{2/3})$, the scaling order for distances is $n^{1/3}$, and the scaling limit is the stable tree of parameter $3/2$.Comment: 59 pages, 14 figure

Latent Space Representations of Hypergraphs

The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis. In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph. In this paper we present a model for hypergraph data which extends the latent space distance model of Hoff et al. (2002) and, by drawing a connection to constructs from computational topology, we develop a model whose likelihood is inexpensive to compute. We obtain posterior samples via an MCMC scheme and we rely on Bookstein coordinates to remove the identifiability issues associated with the latent representation. We demonstrate that the latent space construction imposes desirable properties on the hypergraphs generated in our framework and provides a convenient visualisation of the data. Furthermore, through simulation, we investigate the flexibility of our model and consider estimating predictive distributions. Finally, we explore the application of our model to a real world co-occurrence dataset

From Large to In nite Random Simplicial Complexes.

PhD ThesesRandom simplicial complexes are a natural higher dimensional generalisation to the models of random graphs from Erd}os and R enyi of the early 60s. Now any topological question one may like to ask raises a question in probability - i.e. what is the chance this topological property occurs? Several models of random simplicial complexes have been intensely studied since the early 00s. This thesis introduces and studies two general models of random simplicial complexes that includes many well-studied models as a special case. We study their connectivity and Betti numbers, prove a satisfying duality relation between the two models, and use this to get a range of results for free in the case where all probability parameters involved are uniformly bounded. We also investigate what happens when we move to in nite dimensional random complexes and obtain a simplicial generalisation of the Rado graph, that is we show the surprising result that (under a large range of parameters) every in nite random simplicial complexes is isomorphic to a given countable complex X with probability one. We show that this X is in fact homeomorphic to the countably in nite ball. Finally, we look at and construct nite approximations to this complex X, and study their topological properties

Advancements in latent space network modelling

The ubiquity of relational data has motivated an extensive literature on network analysis, and over the last two decades the latent space approach has become a popular network modelling framework. In this approach, the nodes of a network are represented in a low-dimensional latent space and the probability of interactions occurring are modelled as a function of the associated latent coordinates. This thesis focuses on computational and modelling aspects of the latent space approach, and we present two main contributions. First, we consider estimation of temporally evolving latent space networks in which interactions among a fixed population are observed through time. The latent coordinates of each node evolve other time and this presents a natural setting for the application of sequential monte carlo (SMC) methods. This facilitates online inference which allows estimation for dynamic networks in which the number of observations in time is large. Since the performance of SMC methods degrades as the dimension of the latent state space increases, we explore the high-dimensional SMC literature to allow estimation of networks with a larger number of nodes. Second, we develop a latent space model for network data in which the interactions occur between sets of the population and, as a motivating example, we consider a coauthorship network in which it is typical for more than two authors to contribute to an article. This type of data can be represented as a hypergraph, and we extend the latent space framework to this setting. Modelling the nodes in a latent space provides a convenient visualisation of the data and allows properties to be imposed on the hypergraph relationships. We develop a parsimonious model with a computationally convenient likelihood. Furthermore, we theoretically consider the properties of the degree distribution of our model and further explore its properties via simulation