Statistical Learning in Temporal Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 293
Special Issue Editors
Interests: stochastic processes; queueing theory; statistical inference
Interests: applied probability; probabilistic logics; quality control; queueing networks; stochastic models of computing; stochastic ordering; stochastic processes
Special Issue Information
Dear Colleagues,
Driven by the explosion of data from real-world temporal/dynamic complex networks, there has been recent growing interest in developing new statistical learning methods for these networks. Temporal complex networks are systems that evolve continuously over time, with additions, deletions, and changes in the network’s edges and nodes. Due to their specifics, statistical learning strategies developed previously for static networks do not apply to temporal networks. This Special Issue calls for research papers devoted to the development of new sampling frameworks and learning methods to characterize local and global characteristics, as well as complex relational patterns, such as community detection, of complex temporal networks. Additionally, novel modeling approaches for such networks that aim to understand the emergence of various properties of real-world systems are also welcome.
Dr. Nelson Antunes
Prof. Dr. António Pacheco
Guest Editors
Manuscript Submission Information
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Keywords
- temporal networks
- network analysis
- sampling methods
- estimation
- modeling
