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Controllable data generation by deep learning: A review
Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …
critical applications such as molecule design, image editing and speech synthesis …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
On positional and structural node features for graph neural networks on non-attributed graphs
Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …
such as node classification and graph classification, where the superior performance is …
Curriculum learning for graph neural networks: Which edges should we learn first
Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …
with dependencies by recursively propagating and aggregating messages along the edges …
Large language models for spatial trajectory patterns mining
Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in
mobility behavior with applications in domains like infectious disease monitoring and elderly …
mobility behavior with applications in domains like infectious disease monitoring and elderly …
Deep generative model for periodic graphs
Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and
polygon mesh. Their generative modeling has great potential in real-world applications such …
polygon mesh. Their generative modeling has great potential in real-world applications such …
Multi-objective deep data generation with correlated property control
Develo** deep generative models has been an emerging field due to the ability to model
and generate complex data for various purposes, such as image synthesis and molecular …
and generate complex data for various purposes, such as image synthesis and molecular …
Deep graph representation learning for influence maximization with accelerated inference
Selecting a set of initial users from a social network in order to maximize the envisaged
number of influenced users is known as influence maximization (IM). Researchers have …
number of influenced users is known as influence maximization (IM). Researchers have …
Deep graph translation
Deep generative models for graphs have recently achieved great successes in modeling
and generating graphs for studying networks in biology, engineering, and social sciences …
and generating graphs for studying networks in biology, engineering, and social sciences …
Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
In recent years, a wide variety of graph neural network (GNN) architectures have emerged,
each with its own strengths, weaknesses, and complexities. Various techniques, including …
each with its own strengths, weaknesses, and complexities. Various techniques, including …