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Structural Deep Network Embedding
TLDR
We propose a Structural Deep Network Embedding method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Expand
Heterogeneous Graph Attention Network
TLDR
We first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Expand
Asymmetric Transitivity Preserving Graph Embedding
TLDR
We develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. Expand
Community Preserving Network Embedding
TLDR
In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. Expand
Robust Graph Convolutional Networks Against Adversarial Attacks
TLDR
Graph Convolutional Networks (GCNs) are an emerging type of neural network model on graphs which have achieved state-of-the-art performance in the task of node classification. Expand
Arbitrary-Order Proximity Preserved Network Embedding
TLDR
We propose AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework. Expand
A Survey on Network Embedding
TLDR
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Expand
Social contextual recommendation
TLDR
We propose a novel probabilistic matrix factorization method to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. Expand
Deep Learning on Graphs: A Survey
TLDR
We comprehensively review the different types of deep learning methods on graphs. Expand
Deep Variational Network Embedding in Wasserstein Space
TLDR
We propose a novel Deep Variational Network Embedding in Wasserstein Space (DVNE) in this paper, which can simultaneously preserve the network structure and model the uncertainty of nodes. Expand
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