Autoregressive graph Volterra models and applications

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing...

Dynamic Bi-Colored Graph Partitioning

In this work, we focus on partitioning dynamic graphs with two types of nodes (bi-colored), though not necessarily bipartite graphs. They commonly appear in communication network applications, e.g., one color being base stations, the other users, and the dynamic process being the varying connection status between base stations and moving users....

Waveform-Aware Optimal Window Function Design for Mismatched Filtering

This paper introduces a general framework for waveform-aware optimal window design for mismatch processing. First, the linear relationship between the window function and the output of the mismatch filter, for both frequency and time windowing and a particular waveform, is modeled. Making use of such a relation, typical window constraints are...

Online Graph Learning from Time-Varying Structural Equation Models

Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural...