[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
Towards data-centric graph machine learning: Review and outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …
to drive AI models and applications, has attracted increasing attention in recent years. In this …
Label-free node classification on graphs with large language models (llms)
In recent years, there have been remarkable advancements in node classification achieved
by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels …
by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels …
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 …
Hierarchical representations and explicit memory: Learning effective navigation policies on 3d scene graphs using graph neural networks
Representations are crucial for a robot to learn effective navigation policies. Recent work
has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic …
has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic …
Mag-gnn: Reinforcement learning boosted graph neural network
Abstract While Graph Neural Networks (GNNs) recently became powerful tools in graph
learning tasks, considerable efforts have been spent on improving GNNs' structural …
learning tasks, considerable efforts have been spent on improving GNNs' structural …
No change, no gain: empowering graph neural networks with expected model change maximization for active learning
Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …
graph-structured data, but their success depends on sufficient labeled data. We present a …
JuryGCN: quantifying jackknife uncertainty on graph convolutional networks
Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …
real-world applications. The vast majority of existing works on GCN primarily focus on the …
Reinforcement learning on graphs: A survey
M Nie, D Chen, D Wang - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
Graph mining tasks arise from many different application domains, including social
networks, biological networks, transportation, and E-commerce, which have been receiving …
networks, biological networks, transportation, and E-commerce, which have been receiving …
Efficient subgraph gnns by learning effective selection policies
Subgraph GNNs are provably expressive neural architectures that learn graph
representations from sets of subgraphs. Unfortunately, their applicability is hampered by the …
representations from sets of subgraphs. Unfortunately, their applicability is hampered by the …