4 citations · 7 across the 7 of their papers we have counts for
7 papers
Graph Neural Networks with a Distribution of Parametrized Graphs
See Hian Lee, Feng Ji, Kelin Xia +1
Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, th…
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Hanyang Meng +3
In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label predictio…
Distributional Signals for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Kai Zhao +2
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose sig…
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks
See Hian Lee, Feng Ji, Wee Peng Tay
Many graph neural networks have been developed to learn graph representations in either Euclidean or hyperbolic space, with all nodes' representations embedded in a single space. H…
On semi shift invariant graph filters
Feng Ji, See Hian Lee, Wee Peng Tay
In graph signal processing, one of the most important subjects is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most i…
SGAT: Simplicial Graph Attention Network
See Hian Lee, Feng Ji, Wee Peng Tay
Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches fo…
Learning on heterogeneous graphs using high-order relations
See Hian Lee, Feng Ji, Wee Peng Tay
A heterogeneous graph consists of different vertices and edges types. Learning on heterogeneous graphs typically employs meta-paths to deal with the heterogeneity by reducing the g…