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IEEE Transactions on Signal and Information Processing over Networks

Bibliographic Information

International Title:

IEEE Transactions on Signal and Information Processing over Networks


2373-7778         Period: [2015 .. ]


2373-776X         Period: [2015 .. ]



Country of publication:

United States


Publishing House:

IEEE (Institute of Electrical and Electronics Engineers)

ITAR Code:


NPI Scientific Field:


Minimum Criteria

✅ Scientific editorial board
✅ Peer reviewed
✅ International authorship
✅ Approved ISSN

Open Access

❌ Not indexed by DOAJ - Checked 0 days ago
❌ Not included in any publishing agreement
Plan S: Journal Checker Tool [+]

Scientific level placements and Norwegian HEIs publication points

Year Scientific Level Author Shares Publication Points
2025 2
2024 2
2023 2
2022 1 0.125 0.5814
2021 1 0.575 1.3319
2020 1 0.75 1.4036
2019 1 0.0 0.0
2018 1 2.0 2.0
2017 1 0.0 0.0
Published in May the following year


Comments regarding updates on the publication channel are only visible for you and the advisors. Comments regarding the scientific quality and levels will be public.

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In recent years, distributed and graph based signal processing techniques have attracted great deal of interest in the signal processing community, be it in academic, R&D, or industrial circles. In recognition of such a development the IEEE signal processing community, one of the most reputable community of signal processing professionals in the world, has dedicated the journal in question to publication of manuscript in this area. since its introduction, Transactions on Signal and Information Processing over Networks has build a reputation as the go-to venue for cutting-edge and innovative research, which has established a number of leading trends in distributed learning, filtering, and estimation. The journal in question has also been recognised as an highly influential publications; thus, I think it should be elevated to a level 2 journal.
This journal should be elevated to Level 2. It is a prestigious and high-quality journal that reports world-class research in the area of distributed optimization, distributed processing over network, graph processing, etc. Leading researchers in these fields are targeting this journal.

According to Scimago Journal & Country Rank, this journal belongs to the best quartile Q1.
This is the web site of the journal
IEEE Transactions on Signal and Information Processing over Networks:

which is under the IEEE Signal Processing Society, IEEE Computer Society and IEEE Communications Society.

This journal is currently in Level 1, however, there are very clear reasons for this Journal
to be upgraded to Level 2 in 2021:

- The high quality of this journal is also strongly supported by the
members of the Editorial Board, who are internationally recognised
high-caliber researchers in the area:

- Impact Factor is 3.153

- This journal has become one of the Top reference journals in the areas of
signal processing, distributed data science and decentralized machine learning
and AI over networks, which are very important topics nowadays.

The quality of the papers can be observed immediately by simply scanning over
any of the recent published articles here:

On the other hand,
examples of possible Publications that in my opinion could be moved from
Level 2 to Level 1 are:

- Journal of Heuristics
- Requirements Engineering
- International Journal of Approximate Reasoning
- Genetic Programming and Evolvable Machines
- Empirical Software Engineering
- Eurographics
- Science of Computer Programming
- Software quality Journal
- Science of Computer Programming
- Interacting with computers

Comparatively, the Journal IEEE Transactions on Signal and Information Processing over Networks
has a higher quality as compared to these Journals, based on different parameters, e.g. impact factor,
quality level of Editorial Board, current relevance of results presented in the journal, etc...

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Content on this site is licensed under Norwegian Licence for Open Government Data (NLOD) 2.0 Norwegian Licence for Open Government Data (NLOD) and Creative Commons Attribution 4.0 International (CC BY 4.0) Creative Commons Attribution 4.0 International (CC BY 4.0) ). offers cookies to learn more about their users.I concur No thanks