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Label Embedding with Partial Heterogeneous Contexts
Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, …
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Active discriminative network representation learning
Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination …
Adversarially regularized graph autoencoder for graph embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding …
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Binarized attributed network embedding
Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding …
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Cost-sensitive hybrid neural networks for heterogeneous and imbalanced data
Analyzing accumulated data has recently attracted huge attention for its ability to generate values by identifying useful information …
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Cross-domain deep learning approach for multiple financial market prediction
Over recent decades, globalization has resulted in a steady increase in cross-border financial flows around the world. To build an …
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DiSAN: directional self-attention network for RNN/CNN-free language understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local …
Discrete network embedding
Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The …
FraudNE: a joint embedding approach for fraud detection
Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt …
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Graph ladder networks for network classification
Numerous network representation-based algorithms for network classification have emerged in recent years, but many suffer from two …
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