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Structural Deep Network Embedding
TLDR
This paper proposes a Structural Deep Network Embedding method, namely SDNE, which first proposes a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non- linear network structure and exploits the first-order and second-order proximity jointly to preserve the network structure.
Heterogeneous Graph Attention Network
TLDR
Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of the proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
Asymmetric Transitivity Preserving Graph Embedding
TLDR
A novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), is developed, which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Community Preserving Network Embedding
TLDR
A novel Modularized Nonnegative Matrix Factorization (M-NMF) model is proposed to incorporate the community structure into network embedding and jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures.
Robust Graph Convolutional Networks Against Adversarial Attacks
TLDR
Robust GCN (RGCN), a novel model that "fortifies'' GCNs against adversarial attacks by adopting Gaussian distributions as the hidden representations of nodes in each convolutional layer, which can automatically absorb the effects of adversarial changes in the variances of the Gaussian distribution.
Structural Deep Clustering Network
TLDR
A Structural Deep Clustering Network (SDCN) is proposed to integrate the structural information into deep clustering, with a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model.
A Survey on Network Embedding
TLDR
This survey focuses on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions, covering the structure- and property-preserving network embeding methods, the network embedded methods with side information, and the advanced information preserving network embedting methods.
Deep Learning on Graphs: A Survey
TLDR
This survey comprehensively review the different types of deep learning methods on graphs by dividing the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks,graph autoencoders, graph reinforcement learning, and graph adversarial methods.
Arbitrary-Order Proximity Preserved Network Embedding
TLDR
The eigen-decomposition reweighting theorem is theoretically proved, revealing the intrinsic relationship between proximities of different orders and proposed AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework.
Social contextual recommendation
TLDR
This paper investigates social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence, and proposes a novel probabilistic matrix factorization method to fuse them in latent spaces.
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