Dr. Yilun Shang

University: Tongji University
Department: Department of Mathematical Sciences
Research Fields: Random Graph Theory, Complex Networks, Wireless Networks, Synchronization and Consensus Problems in Multi-agent Systems, Cyber Security
Publications
6 On the Hierarchical Ergodicity Coefficient
Authors: Yilun Shang
Abstract:
In this paper, we deal with the fundamental concepts and properties of ergodicity coefficients in a hierarchical sense by making use of partition. Moreover, we establish a hierarchial Hajnal’s inequality improving some previous results.
Keywords: partition, stochastic matrix, ergodicity coefficient
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9715 The Sizes of Large Hierarchical Long-Range Percolation Clusters
Authors: Yilun Shang
Abstract:
We study a long-range percolation model in the hierarchical lattice ΩN of order N where probability of connection between two nodes separated by distance k is of the form min{αβ−k, 1}, α ≥ 0 and β > 0. The parameter α is the percolation parameter, while β describes the long-range nature of the model. The ΩN is an example of so called ultrametric space, which has remarkable qualitative difference between Euclidean-type lattices. In this paper, we characterize the sizes of large clusters for this model along the line of some prior work. The proof involves a stationary embedding of ΩN into Z. The phase diagram of this long-range percolation is well understood.Keywords: Component, Phase Transition, percolation, hierarchical lattice
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9134 Likelihood Estimation for Stochastic Epidemics with Heterogeneous Mixing Populations
Authors: Yilun Shang
Abstract:
We consider a heterogeneously mixing SIR stochastic epidemic process in populations described by a general graph. Likelihood theory is developed to facilitate statistic inference for the parameters of the model under complete observation. We show that these estimators are asymptotically Gaussian unbiased estimates by using a martingale central limit theorem.Keywords: maximum likelihood, statistic inference, epidemicmodel, heterogeneous mixing
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11193 The Giant Component in a Random Subgraph of a Weak Expander
Authors: Yilun Shang
Abstract:
In this paper, we investigate the appearance of the giant component in random subgraphs G(p) of a given large finite graph family Gn = (Vn, En) in which each edge is present independently with probability p. We show that if the graph Gn satisfies a weak isoperimetric inequality and has bounded degree, then the probability p under which G(p) has a giant component of linear order with some constant probability is bounded away from zero and one. In addition, we prove the probability of abnormally large order of the giant component decays exponentially. When a contact graph is modeled as Gn, our result is of special interest in the study of the spread of infectious diseases or the identification of community in various social networks.
Keywords: percolation, Expander, random graph, subgraph, giant component
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13522 On the Central Limit Theorems for Forward and Backward Martingales
Authors: Yilun Shang
Abstract:
Let {Xi}i≥1 be a martingale difference sequence with Xi = Si - Si-1. Under some regularity conditions, we show that (X2 1+· · ·+X2N n)-1/2SNn is asymptotically normal, where {Ni}i≥1 is a sequence of positive integer-valued random variables tending to infinity. In a similar manner, a backward (or reverse) martingale central limit theorem with random indices is provided.Keywords: Central Limit Theorem, martingale difference sequence, backward martingale
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24601 Topological Properties of an Exponential Random Geometric Graph Process
Authors: Yilun Shang
Abstract:
In this paper we consider a one-dimensional random geometric graph process with the inter-nodal gaps evolving according to an exponential AR(1) process. The transition probability matrix and stationary distribution are derived for the Markov chains concerning connectivity and the number of components. We analyze the algorithm for hitting time regarding disconnectivity. In addition to dynamical properties, we also study topological properties for static snapshots. We obtain the degree distributions as well as asymptotic precise bounds and strong law of large numbers for connectivity threshold distance and the largest nearest neighbor distance amongst others. Both exact results and limit theorems are provided in this paper.Keywords: Wireless Network, Connectivity, autoregressive process, markovian, degree, random geometric graph
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1171