X Yang, Z Song, I King, Z Xu - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in …
Graph representation learning aims to effectively encode high-dimensional sparse graph- structured data into low-dimensional dense vectors, which is a fundamental task that has …
Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The …
F Xia, K Sun, S Yu, A Aziz, L Wan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be …
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition …
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better …
Z Wu, S Pan, F Chen, G Long, C Zhang… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language …
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints …