5 accepted papers
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i.e. when neighbor nodes follow an order. Graphs with ordered neighborhoods are a natural data representation for evolving graphs where edges are created over time, which induces an order. Combining…
Matrix Completion (MC) i.e., estimating the missing values of an unknown matrix based on limited information, has been a prominent topic of study in the last decades. In this paper, we focus more precisely on a little-known aspect of MC, namely how the recommendation of new entries can help improve…
We study online robust matrix completion on graphs. At each iteration a vector with some entries missing is revealed and our goal is to reconstruct it by identifying the underlying low-dimensional subspace from which the vectors are drawn. We assume there is an underlying graph structure to the data…
This work presents a distributed algorithm for nonlinear adaptive learning. In particular, a set of nodes obtain measurements, sequentially one per time step, which are related via a nonlinear function; their goal is to collectively minimize a cost function by employing a diffusion based Kernel Leas…
Accurate coverage maps are an important tool for network planning and operation but it is often impossible to obtain these maps completely from measurements. In this paper we describe two new methods that enable operators to minimize the cost for obtaining a complete coverage map at high accuracy. O…