November 24, 2016

Estimating exponential random graph models using sampled network data via graphon

  • He R.
  • Zheng T.

Analysis of large networks is of interest to many disciplines. Full network data are often hard to collect, storage and analyze. In particular, in many social science surveys, ego nomination techniques have been used to collect the egocentric networks of the randomly sampled survey respondents. In this paper, we propose a sample-GLMLE method that fits exponential random graph models (ERGM) to such sampled egocentric networks. It is an extension of a previous graph-limit based maximum likelihood estimation (GLMLE) method for full network that uses graphon to bridge the estimation of ERGM using observed network data. In this paper, we provide solutions to computational issues that are unique to sampled network data and evaluate the proposed method using simulations. We also apply sample-GLMLE to the public-use set of the National Longitudinal Study of Adolescent Health (AddHealth) study.

View Original Article

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...