Search Results for author:
Found 9 papers, 1 papers with code
Keeping Up With the Winner! Targeted Advertisement to Communities in Social Networks
When a new product enters a market already dominated by an existing product, will it survive along with this dominant product?
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion.
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Two-timescale stochastic approximation (TTSA) is among the most general frameworks for iterative stochastic algorithms.
Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures.
Convergence of Bi-Virus Epidemic Models with Non-Linear Rates on Networks -- A Monotone Dynamical Systems Approach
We study convergence properties of competing epidemic models of the Susceptible-Infected-Susceptible (SIS) type.
Efficiency Ordering of Stochastic Gradient Descent
We consider the stochastic gradient descent (SGD) algorithm driven by a general stochastic sequence, including i. i. d noise and random walk on an arbitrary graph, among others; and analyze it in the asymptotic sense.
Bi-SIS Epidemics on Graphs -- Quantitative Analysis of Coexistence Equilibria
We consider a system in which two viruses of the Susceptible-Infected-Susceptible (SIS) type compete over general, overlaid graphs.
Competing Epidemics on Graphs -- Global Convergence and Coexistence
The dynamics of the spread of contagions such as viruses, infectious diseases or even rumors/opinions over contact networks (graphs) have effectively been captured by the well known \textit{Susceptible-Infected-Susceptible} ($SIS$) epidemic model in recent years.
Beyond Random Walk and Metropolis-Hastings Samplers: Why You Should Not Backtrack for Unbiased Graph Sampling
In this paper, we propose non-backtracking random walk with re-weighting (NBRW-rw) and MH algorithm with delayed acceptance (MHDA) which are theoretically guaranteed to achieve, at almost no additional cost, not only unbiased graph sampling but also higher efficiency (smaller asymptotic variance of the resulting unbiased estimators) than the SRW-rw and the MH algorithm, respectively.
Methodology Data Structures and Algorithms Networking and Internet Architecture Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
