Structured Egonet Tensors for Robust Node Embedding

Fatemeh Sheikholeslami, Georgios B. Giannakis

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

Abstract

Recent advances in algorithmic and computational tools have led to an unprecedented growth in data mining over networks. However, partial knowledge of node connectivity (due to privacy concerns or the large number of nodes), as well as incomplete domain knowledge (as in e.g., biological applications), challenge learning tasks over real networks. For robust learning from incomplete data, node embedding over graphs is thus well motivated, and is pursued here by leveraging tensors as multi-dimensional data structures. To this end, a novel tensor-based network representation is advocated, over which node embedding is cast as a structured nonnegative tensor decomposition. The trilinear factorization involved is performed using an alternating least-squares approach. The extracted node embeddings are then utilized to predict the missing links. Performance is assessed via numerical tests on benchmark networks, corroborating the effectiveness and robustness of the proposed technique over incomplete graphs.

Original languageEnglish (US)
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages465-469
Number of pages5
ISBN (Electronic)9781728155494
DOIs
StatePublished - Dec 2019
Event8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe
Duration: Dec 15 2019Dec 18 2019

Publication series

Name2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

Conference

Conference8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Country/TerritoryGuadeloupe
CityLe Gosier
Period12/15/1912/18/19

Keywords

  • link prediction
  • Node embedding
  • tensor decomposition

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