Jan 5, 2021 · This work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network.
We evaluate its performance on the task of inductive node classification and community detection via three real-world attributed networks. Experimental results ...
Oct 22, 2024 · Thus, this work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network. It is ...
Inductive representation learning via CNN for partially-unseen attributed networks. Z Zhao, H Zhou, L Qi, L Chang, MC Zhou. IEEE Transactions on Network ...
CNN-based Community Detection. Paper Title, Venue, Year, Method, Materials. Inductive representation learning via CNN for partially-unseen attributed networks ...
Jan 8, 2021 · We propose a deep model based embedding learning method for attributed networks, named DeepEmLAN. It can smoothly project different types of attributed ...
Here we present GraphSAGE, a general inductive framework that leverages node feature information (eg, text attributes) to efficiently generate node embeddings ...
Missing: CNN | Show results with:CNN
Thus, this work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network. It is designed based on a ...
A novel framework to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label ...
DeepEmLAN: deep embedding learning for attributed networks. ... Inductive representation learning via CNN for partially-unseen attributed networks.