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A Short Journey through Whole Graph Embedding Techniques Template
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Aula Multimediale (Dipartimento di Fisica)
Aula Multimediale
Dipartimento di Fisica
Description
Speaker: Mario Guarracino
Abstract
Networks provide suitable models in many applications, ranging from social to life and physical sciences. Such representations are able to capture interactions and dependencies among variables or observations, and can be extended to consider ensembles of networks, thus providing simple and powerful modeling of phenomena. Whole graph embedding involves the projection of ensembles 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 then be used for feature extraction, graph clustering or for building classification models. In these lectures, we survey embedding techniques which jointly embed whole graphs for classification tasks. We compare them and evaluate their performance on undirected synthetic and real world network datasets on different learning tasks.