IsoGloVe: a new count-based graph embedding method based on geodesic distance

Abstract: Graph embedding techniques have gained increasing attention for their ability to encode the complex structural information of networks into low-dimensional vectors. Existing graph embedding methods have achieved considerable success in various applications. However, these methods have limitations in capturing global graph topology information and fail to provide insights into the underlying mechanisms of network function. In this paper, we propose IsoGloVe, a count-based method that encodes graph topology into vectors using the co-occurrence statistics of fixed-size routes in random walks. IsoGloVe calculates the final embeddings based on the geodesic distances of the node’s neighbors on a manifold. This representation in geodesic space allows for the analysis of node interactions and contributes to a better understanding of complex network structure and function. The performance of IsoGloVe is evaluated on various protein-protein interactions (PPI) using graph reconstruction, node classification, and visualization. The findings reveal that IsoGloVe surpasses other comparable methods with a 30% increase in MAP for graph reconstruction and a 25% increase in model scores for node classification in the Yeast PPI network. In addition, IsoGloVe demonstrated a 6.9% increase in MAP for graph reconstruction on the Human PPI network

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
Proceedings of the 36th Canadian Conference on Artificial Intelligence. - [Kitchener, ON], 2023

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2024
Urheber
Nahali, Sepideh
Safari, Leila
Khanteymoori, Alireza
Ayadi, Hajer
Huang, Jimmy

DOI
10.21428/594757db.841b6ef2
URN
urn:nbn:de:bsz:25-freidok-2547742
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:30 MESZ

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Beteiligte

  • Nahali, Sepideh
  • Safari, Leila
  • Khanteymoori, Alireza
  • Ayadi, Hajer
  • Huang, Jimmy
  • Universität

Entstanden

  • 2024

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