IEEE Transactions on Signal and Information Processing over Networks

Special Announcements:

We are pleased to announce that, as of January 2019, the IEEE Transactions on Signal and Information Processing over Networks (TSIPN) has formally been accepted for indexing by the Clarivate Analytics Web of Science.

Articles published in TSIPN as of March 2016 will be covered in the following Clarivate Analytics products:

  • Science Citation Index Expanded (aka SciSearch®)
  • Journal Citation Reports/Science Edition
  • Current Contents®/Engineering Computing and Technology

TSIPN will be assigned an impact factor in June 2019, following Clarivate's yearly assessment of citation tracking via the Journal Citation Reports. The impact factor will be posted on the TSIPN website as soon as it has been released by Clarivate.

To access indexing data or learn more about the indexing platform, please visit the Clarivate Analytics Web of Science website.

Scope

IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.

Reproducible research

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication's figures and tables on a website; see the supplemental materials section of the Information for Authors page. It gives other researchers easier access to the work, and facilitates fair comparisons.

Multimedia content

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Digital Toolbox.

TSIPN Volume 11 | 2025

Memory-Enhanced Distributed Accelerated Algorithms for Coordinated Linear Computation

In this paper, a memory-enhanced distributed accelerated algorithm is proposed for solving large-scale systems of linear equations within the context of multi-agent systems. By employing a local predictor consisting of a linear combination of the nodes' current and previous values, the inclusion of two memory taps can be characterized such that the convergence of the distributed solution algorithm for coordinated computation is accelerated.

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Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks

Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the information from higher-order neighbors in the graph, involving the incorporation of powers of the shift operator, such as the graph Laplacian or adjacency matrix.

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A Fixed-Time Convergent Distributed Algorithm for Time-Varying Optimal Resource Allocation Problem

This article proposes a distributed time-varying optimization approach to address the dynamic resource allocation problem, leveraging a sliding mode technique. The algorithm integrates a fixed-time sliding mode component to ensure that the global equality constraints are met, and is coupled with a fixed-time distributed control mechanism involving the nonsmooth consensus idea for attaining the system's optimal state.

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TSIPN Volume 10 | 2024