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Signals evolving over graphs emerge naturally in a number of applications related to network science. A frequently encountered challenge pertains to reconstructing such signals given their values on subsets of vertices at possibly different time instants. Spatiotemporal dynamics can be leveraged so that a small number of vertices suffices to achieve accurate reconstruction. The present paper broadens...
We consider the problem of recovering a smooth graph signal from noisy samples taken at a small number of graph nodes. The recovery problem is formulated as a convex optimization problem which minimizes the total variation (accounting for the smoothness of the graph signal) while controlling the empirical error. We solve this total variation minimization problem efficiently by applying a recent algorithm...
In this paper, we discuss how to design the graph topology to reduce the communication complexity of certain algorithms for decentralized optimization. Our goal is to minimize the total communication needed to achieve a prescribed accuracy. We discover that the so-called expander graphs are near-optimal choices. We propose three approaches to construct expander graphs for different numbers of nodes...
We conducted one of the most comprehensive many-antenna MU-MIMO channel measurement campaigns ever reported. Our measurement system supports full mobility with very high time-frequency resolution. We report channel traces in the UHF, 2.4 GHz, and 5 GHz bands, in diverse environments, with up to 104 base-station antennas serving 8 users. We characterize channel stability and capacity across these bands...
Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes...
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are...
This paper presents a simple and robust algorithm for determining a leader node in a cooperative network based on MDL (Minimum Description Length) subspace algorithm. The algorithm aims to improve the performance of the cooperating network in a spectrum sensing problem for cognitive radio. The outline of the communication and selection process is described and the SNR (signal to noise ratio) estimation...
High order networks are weighted hypergraphs collecting relationships between elements of tuples, not necessarily pairs. Valid metric distances between high order networks have been defined but they are difficult to compute when the number of nodes is large. The goal here is to find tractable approximations of these network distances. The paper does so by mapping high order networks to filtrations...
This paper studies the relationship between the topological structure of a social networks, and the information flow within it. In our recent work [7], we showed that a particular core-periphery decomposition using topological collapses has (a) structural properties desired in the decomposition, and (b) communal properties in the peripheral components. In this paper, we investigate the role of the...
The goal of this paper is to propose adaptive strategies for distributed learning of signals defined over graphs. Assuming the graph signal to be band-limited, the method enables distributed adaptive reconstruction from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on...
This paper studies network topology inference, which is a cornerstone problem in statistical analysis of complex systems. The fresh look advocated here builds on recent advances in convex optimization and graph signal processing to identify the so-termed graph-shift operator (encoding the network topology) given only the eigenvectors of the shift. These spectral templates can be obtained, for example,...
Solar storms can induce quasi-dc geomagnetically induced current (GIC) flows in power grids, which could potentially lead to transformer damage and system stability and reliability issues. We consider the problem of designing operational GIC mitigation strategies by switching transmission lines. This topology control approach could relieve the power network from temporarily high level of GIC flows,...
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