December 15, 2016

Fast approximation algorithms for p-centres in large delta-hyperbolic graphs

  • Edwards K.
  • Kennedy W.
  • Saniee I.

We provide a quasi-linear time algorithm for the p-center clustering problem with an additive error less than or equal to 3 times the input graph's hyperbolic constant. Specifically, for the graph G = (V;E) with n vertices, m edges and hyperbolic constant delta, we construct an algorithm for p-center clustering that runs in time O(p(delta + 1)(n + m) log(n)) with radius not exceeding r_p +delta_ when p is less than or equal to 2 and r_p + 3delta when p is greater than 3, where r_p is the optimal radius of the p-center clusters. Prior work had identified p-centers with accuracy r_p+delta but with cubic time complexity, O((n^3 log n + n^2 m) log(diam(G))), which is impractical for large graphs. We also provide computational results for networks with 10Ks to 2M edges showing the proposed algorithm not only runs considerably faster than the prior work as shown by theory, but also provides similar clustering results in practice.

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 ...