21 citations · 31 across the 5 of their papers we have counts for
5 papers
When Does A Spectral Graph Neural Network Fail in Node Classification?
Zhixian Chen, Tengfei Ma, Yang Wang
Spectral Graph Neural Networks (GNNs) with various graph filters have received extensive affirmation due to their promising performance in graph learning problems. However, it is k…
Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embedding
Zipeng Liu, Yang Wang, Jürgen Bernard +1
Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links. GNN developers rely on quantitative…
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks
Chris Connell, Yang Wang
Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as lin…
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
Zhixian Chen, Tengfei Ma, Yangqiu Song +1
Missing node attributes is a common problem in real-world graphs. Graph neural networks have been demonstrated power in graph representation learning while their performance is aff…
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Zhixian Chen, Tengfei Ma, Zhihua Jin +2
Graph convolutional networks (GCNs) have achieved great success on graph-structured data. Many graph convolutional networks can be thought of as low-pass filters for graph signals.…