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Temporal network embedding framework with causal anonymous walks representations
PeerJ Computer Science. 2022. Vol. 8. Article e858.
Altukhov D., Kleeva D., Ossadtchi A., Neuroimage 2023 Vol. 280 Article 120333
Functional connectivity is crucial for cognitive processes in the healthy brain and serves as a marker for a range of neuropathological conditions. Non-invasive exploration of functional coupling using temporally resolved techniques such as MEG allows for a unique opportunity of exploring this fundamental brain mechanism.
The indirect nature of MEG measurements complicates the estimation of functional coupling due to the volume ...
Added: September 24, 2023
Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, 2022.
Added: April 29, 2022
Makarov I., Savchenko A., Arseny Korovko et al., PeerJ Computer Science 2022 Vol. 8 Article e858
Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. ...
Added: January 20, 2022
Batagelj V., Maltseva D., Journal of Informetrics 2020 Vol. 14 No. 1 P. 1–14
We present two ways (instantaneous and cumulative) to transform bibliographic networks, using the works’ publication year, into corresponding temporal networks based on temporal quantities. We also show how to use the addition of temporal quantities to define interesting temporal properties of nodes, links and their groups thus providing an insight into evolution of bibliographic networks. ...
Added: October 22, 2019
Aleskerov F. T., Швыдун С. В., , in: Studies in Computational Intelligence* 1. Vol. 812: Complex Networks and Their Applications VII.: Springer, 2019. P. 94–103.
We propose a model that evaluates how much a network has changed over time in terms of its structure and a set of central elements. The difference of structure is evaluated in terms of node-to-node influence using known nodes correspondence models. To analyze the changes in nodes centralities we adapt an idea of interval orders ...
Added: December 4, 2018
Batagelj V., PRAPROTNIK S., Social Network Analysis and Mining 2016 Vol. 6 No. 1 P. 1–22
n a temporal network, the presence and activity of nodes and links can change through time. To describe temporal networks we introduce the notion of temporal quantities. We define the addition and multiplication of temporal quantities in a way that can be used for the definition of addition and multiplication of temporal networks. The corresponding ...
Added: November 1, 2018
Batagelj V., Praprotnik S., Ars Mathematica Contemporanea 2016 Vol. 11 No. 1 P. 11–33
In our previous article we defined temporal quantities used for the description of temporal networks with zero latency and we showed that some centrality measures (e.g. degree, betweenness, closeness) can be extended to the case of temporal networks. In this article we broaden the scope of centrality measures in temporal networks to centrality measures derived ...
Added: November 1, 2018