Centrality informed embedding of networks for temporal feature extraction
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 charact...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154815 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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