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A Comprehensive Survey on Graph Neural Networks
TLDR
We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely, recurrent GNNs, convolutional Gnns, graph autoencoders, and spatial-temporal GNN. Expand
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DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding
TLDR
We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise). Expand
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Adversarially Regularized Graph Autoencoder for Graph Embedding
TLDR
We propose a novel adversarial graph embedding framework for graph data. Expand
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Graph WaveNet for Deep Spatial-Temporal Graph Modeling
TLDR
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Expand
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Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop
TLDR
We developed and implemented a scalable item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. Expand
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Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
TLDR
In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. Expand
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MGAE: Marginalized Graph Autoencoder for Graph Clustering
TLDR
We propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering. Expand
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Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
TLDR
We propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding. Expand
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Optimal Cloud Resource Auto-Scaling for Web Applications
TLDR
We propose an optimal VM-level autoscaling scheme at the virtual machine (VM) level for web application providers. Expand
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Attributed Graph Clustering: A Deep Attentional Embedding Approach
TLDR
We propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Expand
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