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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. Expand
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. Expand
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. Expand
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. Expand
Multimedia Cloud Computing
TLDR
A multimedia-aware cloud is presented, which addresses how a cloud can perform distributed multimedia processing and storage and provide quality of service (QoS) provisioning for multimedia services, and a media-edge cloud (MEC) architecture is proposed, in which storage, central processing unit (CPU), and graphics processing units (GPU) clusters are presented at the edge. Expand
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. Expand
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression
TLDR
A novel attention based location regression network is designed to predict the temporal coordinates of sentence query from the previous attention and a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization. Expand
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
TLDR
A novel semantic conditioned dynamic modulation mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time, is proposed. Expand
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. Expand
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. Expand
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