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Title: Centrality informed embedding of networks for temporal feature extraction
Authors: Oggier, Frédérique 
Datta, Anwitaman 
Keywords: Science::Mathematics
Engineering::Computer science and engineering
Issue Date: 2021
Source: Oggier, F. & Datta, A. (2021). Centrality informed embedding of networks for temporal feature extraction. Social Network Analysis and Mining, 11, 12-.
Journal: Social Network Analysis and Mining
Abstract: We propose a two-step methodology for exploring the tem- poral characteristics of a network. First, we construct a graph time series, where each snapshot is the result of a temporal whole-graph embedding. The embedding is carried out using the degree, Katz and betweenness centralities to characterize first and higher order proximities among ver- tices. Then a principal component analysis is performed over the collected temporal graph samples, which exhibits eigengraphs, graphs whose tem- poral weight variations model the sampled graph series. Analysis of the temporal timeline of each of the main eigengraphs reveals moments of importance in terms of structural graph changes. Parameters such as the dimension of the embeddings and the number of temporal samples are explored. Two case studies are presented: a Bitcoin subgraph, where findings are cross-checked by looking at the subgraph behavior itself, and the Enron email network, which allows us to compare our findings with prior studies. In both cases, the proposed methodology successfully identified temporal structural changes in the graph evolution.
ISSN: 1869-5450
DOI: 10.1007/s13278-021-00720-8
DOI (Related Dataset): 10.21979/N9/9NK2DD
Schools: School of Physical and Mathematical Sciences 
School of Computer Science and Engineering 
Rights: © 2021 Springer. This is a post-peer-review, pre-copyedit version of an article published in Social Network Analysis and Mining. The final authenticated version is available online at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles
SPMS Journal Articles

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