Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks …

Recently, researches have explored the graph neural network (GNN) techniques on text
classification, since GNN does well in handling complex structures and preserving global …

Although Transformer has achieved great successes on many NLP tasks, its heavy structure
with fully-connected attention connections leads to dependencies on large training data. In …

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that
sequentially track character and word information have achieved great success. However …

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict
the price movement in a daily basis, although the market impact of news can be absorbed …

Social trust assessment that characterizes a pairwise trustworthiness relationship can spur
diversified applications. Extensive efforts have been put in exploration, but mainly focusing …