Patel, A.; Mathiraj, R.R.; Jai, M.; Singh, K.; Sivadasan, N.; Balasubramanian, V.N. HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs. Preprints2018, 2018020023. https://doi.org/10.20944/preprints201802.0023.v1
APA Style
Patel, A., Mathiraj, R.R., Jai, M., Singh, K., Sivadasan, N., & Balasubramanian, V.N. (2018). HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs. Preprints. https://doi.org/10.20944/preprints201802.0023.v1
Chicago/Turabian Style
Patel, A., Naveen Sivadasan and Vineeth N Balasubramanian. 2018 "HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs" Preprints. https://doi.org/10.20944/preprints201802.0023.v1
Abstract
This paper presents a new method : HIVEC to learn latent vector representations of graphs in a manner that captures the semantic dependencies of sub-structures. The representations can then be used in machine learning algorithms for tasks such as graph classification, clustering etcetera. The method proposed is unsupervised and uses the information of co-occurrence of sub-structures. It introduces a notion of hierarchical embeddings that allows us to avoid repetitive learning of sub-structures for every new graph. As an alternative to deep learning methods, the edit distance similarity between sub-structures is also used to learn vector representations. We compare the performance of these methods against previous work.
Keywords
deep learning; graph kernels; unsupervised learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
