On Whole-Graph Embedding Techniques

L. Maddalena (), I. Manipur, M. Manzo and M. R. Guarracino
Additional contact information
L. Maddalena: Institute for High-Performance Computing and Networking (ICAR), National Research Council
I. Manipur: Institute for High-Performance Computing and Networking (ICAR), National Research Council
M. Manzo: University of Naples “L’Orientale”, ITS
M. R. Guarracino: University of Cassino and Southern Lazio

A chapter in Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells, 2021, pp 115-131 from Springer

Abstract: Abstract Networks provide suitable representative models in many applications, ranging from social to life sciences. Such representations are able to capture interactions and dependencies among variables or observations, thus providing simple and powerful modeling of phenomena. Whole-graph embedding involves the projection of graphs into a vector space, while retaining their structural properties. In recent years, several embedding techniques using graph kernels, matrix factorization, and deep learning architectures have been developed to learn low-dimensional graph representations. These embeddings can be used for feature extraction, graph clustering, or building classification models. In this chapter, we survey embedding techniques that jointly embed whole graphs for classification tasks. We compare them and evaluate their performance on undirected synthetic and real-world network datasets. The datasets and software adopted for our experiments are made publicly available for further comparisons.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73241-7_8

Ordering information: This item can be ordered from
http://www.springer.com/9783030732417

DOI: 10.1007/978-3-030-73241-7_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().