132 citations · 163 across the 7 of their papers we have counts for
7 papers
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li +8
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Mingqi Yang, Wenjie Feng, Yanming Shen +1
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Trans…
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
Xueyan Yin, Feifan Li, Yanming Shen +2
Recently, deep learning methods have made great progress in traffic prediction, but their performance depends on a large amount of historical data. In reality, we may face the data…
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li +9
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can on…
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Mingqi Yang, Yanming Shen, Rui Li +3
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and t…
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track
Chengxuan Ying, Mingqi Yang, Shuxin Zheng +7
In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as our basic models. We train each mode…
Do Transformers Really Perform Bad for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo +5
The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance…