167 citations · 849 across the 39 of their papers we have counts for
12 papers · 1 filter
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Xingcheng Fu, Yuecen Wei, Qingyun Sun +4
Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topo…
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Dongcheng Zou, Hao Peng, Xiang Huang +5
Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather th…
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Cheng Ji, Jianxin Li, Hao Peng +4
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. Howeve…
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Qingyun Sun, Jianxin Li, Beining Yang +3
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption ca…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
Tianyu Chen, Shaohan Huang, Yuan Xie +5
The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of paramete…
Curvature Graph Generative Adversarial Networks
Jianxin Li, Xingcheng Fu, Qingyun Sun +4
Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph represent…
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing
Xiangyu Song, Jianxin Li, Qi Lei +3
The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracin…
Interpretable and Efficient Heterogeneous Graph Convolutional Network
Yaming Yang, Ziyu Guan, Jianxin Li +3
Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Inform…
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
Jianxin Li, Cheng Ji, Hao Peng +4
Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also…
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Yu He, Yangqiu Song, Jianxin Li +3
Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attent…
Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Hao Peng, Jianxin Li, Hao Yan +5
Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven ext…