Netpro2vec: A Graph Embedding Framework for Biomedical Applications

Ichcha Manipur

doi:10.1109/TCBB.2021.3078089

Netpro2vec: A Graph Embedding Framework for Biomedical Applications

2022-03-01

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Mario Rosario Guarracino

DOI：10.1109/TCBB.2021.3078089

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The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

Keywords:

Kernel

Task analysis

Biology

Deep learning

Data models

Predictive models

Biomedical imaging

Graphs and networks

classification

graph embedding

neural networks

metabolic networks

Netpro2vec: A Graph Embedding Framework for Biomedical Applications

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