98%
921
2 minutes
20
We seek to understand the topological and temporal nature of temporal networks by computing the distances, latencies and lengths of paths. Shortest fastest paths offer interesting insights about connectivity that were unknowable until recently. Moreover, distances and latencies tend to be computed by separate algorithms. We developed four algorithms that each compute all those values efficiently as a contribution to the literature. Two of those methods compute metrics from a fixed source temporal node. The other two, as a significant contribution to the literature, compute the metrics between all pairs of source and destination temporal nodes. The methods are also grouped by whether they work on paths with or not. Proofs of correctness for our algorithms are presented as well as bounds on their temporal complexities as functions of temporal network parameters. Experimental results show the algorithms presented perform well against the state of the art and terminate in decent time on real-world datasets. One purpose of this study is to help develop algorithms to compute centrality functions on temporal networks such as the betweenness centrality and the closeness centrality.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175937 | PMC |
http://dx.doi.org/10.1007/s13278-021-00759-7 | DOI Listing |
PLoS One
September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
View Article and Find Full Text PDFPLoS One
September 2025
College of Landscape Architecture and Art, Northwest Agriculture and Forestry University, Xianyang, China.
This study investigates the spatial and temporal distribution and the influencing factors of 579 cultural heritage sites along the Qin-Shu Ancient Road in Shaanxi Province, employing kernel density estimation, buffer analysis, and geographic detectors. Three key findings emerge: (1) The spatial pattern is characterized by a "line-belt-core" structure, with a belt-like aggregation along the Xi'an-Baoji-Hanzhong axis. Core concentrations are found in Xi'an (181 sites), Hanzhong (159 sites), and Ankang (122 sites), with secondary concentrations in Baoji (72 sites) and Shangluo (36 sites).
View Article and Find Full Text PDFJ Alzheimers Dis
September 2025
Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
Compared with more typical late-onset Alzheimer's disease (AD), the mechanisms of young-onset AD (YOAD; age of symptom onset <65 years) remain less understood. Using resting-state functional MRI data and dynamic causal modeling techniques, Sacu et al. demonstrate that individuals with YOAD (amnestic AD or posterior cortical atrophy) exhibit alterations in effective (i.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2025
Force prediction is crucial for functional rehabilitation of the upper limb. Surface electromyography (sEMG) signals play a pivotal role in muscle force studies, but its non-stationarity challenges the reliability of sEMG-driven models. This problem may be alleviated by fusion with electrical impedance myography (EIM), an active sensing technique incorporating tissue morphology information.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose a novel long-term spatio-temporal model operating in a totally unsupervised way.
View Article and Find Full Text PDF