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- W3211161246 abstract "Recently, graph embedding models significantly improved the quality of graph machine learning tasks, such as node classification and link prediction. In this work, we propose a model called JONNEE (JOint Network Nodes and Edges Embedding), which learns node and edge embeddings under self-supervision via joint constraints in a given graph and its edge-to-vertex dual representation as a Line graph. The model uses two graph autoencoders with additional structural feature engineering and several regularization techniques to train for an adjacency matrix reconstruction task in an unsupervised setting. Experimental results show that our model performs on par with state-of-the-art undirected attribute graph embedding models and requires less number of epochs to achieve the same quality due to Line graph self-supervision under a unified embedding framework." @default.
- W3211161246 created "2021-11-08" @default.
- W3211161246 creator A5048889100 @default.
- W3211161246 creator A5052717053 @default.
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- W3211161246 date "2021-01-01" @default.
- W3211161246 modified "2023-10-16" @default.
- W3211161246 title "JONNEE: Joint Network Nodes and Edges Embedding" @default.
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- W3211161246 doi "https://doi.org/10.1109/access.2021.3122100" @default.
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