IEEE Transactions on Signal and Information Processing over Networks

Graph distance (or similarity) scores are used in several graph mining tasks, including anomaly detection, nearest neighbor and similarity search, pattern recognition, transfer learning, and clustering. Graph distances that are metrics and, in particular, satisfy the triangle inequality, have theoretical and empirical advantages. 

Control over noisy communication-channels” invented by Sahai-Mitter-and-Tatikonda is a prominent topic. In this context, the latency-and-reliability trade-off is considered by responding to the following: How much fast? How much secure? For a stochastic-mean-field-game (S-MFG), we assign the source-codes as the agents. Additionally, the total-Reward is the Volume of the maximum secure lossy source-coding-rate achievable between a set of Sensors, and the Fusion-Centre (FC) set – including intercepting-Byzantines.

The localizability analysis for wireless sensor network is of great significance to network localization, and topology control. In this paper, the localizability problem for the bearing-based localization is investigated. An identification method for bearing rigid component is presented, and the localizability is studied for the determined bearing rigid component. In the identification process for bearing rigid component, the center node is introduced, and an approach for identifying the bearing rigid component is proposed based on the characteristic of the bearing rigid graph by using the center nodes.

In this article, an interval estimation problem is investigated for a class of discrete-time nonlinear networked systems under stealthy attacks. An improved event-triggered protocol with the time-varying threshold is adopted to govern the received signals of interval observer so as to reduce unnecessary data communication burden.

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems.

This article presents limited feedback-based precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the min-max mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization.

This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.

In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem.

We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. 

Observability is a fundamental concept in system inference and estimation. This article is focused on structural observability analysis of Cartesian product networks. Cartesian product networks emerge in variety of applications including in parallel and distributed systems.