87 citations · 242 across the 13 of their papers we have counts for
4 papers · 1 filter
Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan, Tengfei Ma, Liangcai Gao +2
In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based…
Link Prediction with Persistent Homology: An Interactive View
Zuoyu Yan, Tengfei Ma, Liangcai Gao +2
Link prediction is an important learning task for graph-structured data. In this paper, we propose a novel topological approach to characterize interactions between two nodes. Our…
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.…