24 citations · 28 across the 2 of their papers we have counts for
4 papers
Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
Yucheng Lin, Huiting Hong, Xiaoqing Yang +3
Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/e…
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
Huiting Hong, Hantao Guo, Yucheng Lin +3
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.…
Interpretable Text Classification Using CNN and Max-pooling
Hao Cheng, Xiaoqing Yang, Zang Li +2
Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the i…
AHINE: Adaptive Heterogeneous Information Network Embedding
Yucheng Lin, Xiaoqing Yang, Zang Li +1
Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensiona…