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Learning Node Embeddings with Exponential Family Distributions

Abdulkadir Çelikkanat 1, 2, 3 Fragkiskos Malliaros 1, 2, 3 
2 OPIS - OPtimisation Imagerie et Santé
Inria Saclay - Ile de France, CVN - Centre de vision numérique
Abstract : Representing networks in a low dimensional latent space is a crucial task with many interesting application in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks with the traditional Skip-Gram approach, modeling center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding (EFGE) model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. Our experimental evaluation demonstrates that the proposed technique outperforms well-known baseline methods in two downstream machine learning tasks.
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Submitted on : Monday, October 28, 2019 - 3:49:06 PM
Last modification on : Friday, February 4, 2022 - 3:25:25 AM
Long-term archiving on: : Wednesday, January 29, 2020 - 6:07:58 PM


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  • HAL Id : hal-02336000, version 1


Abdulkadir Çelikkanat, Fragkiskos Malliaros. Learning Node Embeddings with Exponential Family Distributions. NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems - Workshop on Graph Representation Learning, Dec 2019, Vancouver, Canada. ⟨hal-02336000⟩



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