Analysis of information and computing networks (ICNs), like social networks (such as Facebook and Twitter), protein interaction networks, and human brain networks, helps us to understand various real-world systems. It also benefits real-world domains such as data analytics, process analytics, service computing, and knowledge discovery. Networks derived from real-world systems always evolve with changing network topological structures and temporal entities’ semantic features, which requires novel and advanced techniques to handle this issue for time-varying information and computing networks (TVICNs). Community structure has been observed as a common phenomenon to describe the cohesion of entities with complicated relationships and features in ICNs. It provides valuable information for widespread practical applications such as business, sociology, biology, and health, which assigns community detection a more important role in ICN analysis. According to the recent investigation over the last decade, the development of community detection research has been brought into a prospering era, which provides a great opportunity for TVICN analysis.
Although recent studies on community detection have paid more attention to combining the network topology information and entities’ semantic features in static environments, community detection in TVICNs faces much more complicated real-world scenarios with significant challenges to address. Besides the network evolution, TVICNs encounter multiple network layers, connections between multiple types of items, cross-domain information, multi-view attributes, and increasing network scale. All these challenges require better solutions for the further development of this field. Therefore, it is quite urgent and essential to design novel and effective solutions for community detection in TVICNs.
The aim of this special section is to solicit contributions to fundamental research in community detection in TVICNs. We seek studies on 1) community detection in TVICNs applied to business, sociology, biology, health, and other industrial applications that help to solve real-world problems; 2) new algorithmic foundations and representation formalisms to address issues on network evolution, complicated information, and changing characteristics for TVICNs; 3) TVICNs’ contributions to real-world domains; and 4) theoretically interpreting the power and limitations of community detection methodologies for TVICNs. Topics of interest include (but are not limited to):
- Applied TVICNs and methodologies to solve real-world problems in business, sociology, biology, health, and other industrial applications, such as (but not limited to):
– recommender systems,
– social network analysis,
– protein function prediction,
– cancer detection, and
– anomaly detection.
- Real-world TVICNs and related methodologies to address issues regarding network evolution, heterogeneous/complex information, and changing characteristics, such as (but not limited to):
– changes in computing networks topology structures and/or in temporal entities’ semantic features,
– complex computing network architecture based on multiple layers,
– computing networks characterized by heterogeneous, cross-domain, multi-view information, and increasing computing network scale.
- TVICNs for data analytics, process analytics, service computing, and knowledge discovery in real-world domains.
Deadline for submissions: 31 December 2021
First decision (accept/reject/revise, tentative): 31 March 2022
Submission of revised papers: 31 May 2022
Notification of final decision (tentative): 31 July 2022
Journal publication (tentative): second half of 2022
For author information and guidelines on submission criteria, please visit the Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Contact the guest editors at email@example.com.
Jia Wu, Macquarie University, Australia (IEEE Senior Member)
Jian Yang, Macquarie University, Australia (IEEE Member)
Philip S. Yu, University of Illinois at Chicago, USA (IEEE Fellow)
Corresponding TETC editor:
Carlo Condo, Infinera/Carleton University, Canada (IEEE Senior Member)