HCORE-Init: Neural network initialization based on graph degeneracy

Stratis Limnios, George Dasoulas, Dimitrios M. Thilikos, Michalis Vazirgiannis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Neural networks have become a very popular tool for many machine learning tasks, as in recent years we witnessed many novel architectures, learning and optimization techniques for deep learning. Capitalizing on the fact that neural networks inherently constitute multipartite graphs among neuron layers, we aim to analyze directly their structure to extract meaningful information that can improve the learning process. To our knowledge graph mining techniques for enhancing learning in neural networks have not been thoroughly investigated. In this paper we propose an adapted version of the k-core structure for the complete weighted multipartite graph extracted from a deep learning architecture. As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition, the hypergraph analogue of k-core degeneracy. We applied k-hypercore to several neural network architectures, more specifically to convolutional neural networks and multilayer perceptrons for image recognition tasks after a very short pretraining. Then we used the information provided by the hypercore numbers of the neurons to re-initialize the weights of the neural network, thus biasing the gradient optimization scheme. Extensive experiments proved that k-hypercore outperforms the state-of-the-art initialization methods.

Original languageEnglish (US)
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728188089
StatePublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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