59 citations · 166 across the 17 of their papers we have counts for
17 papers
UPREVE: An End-to-End Causal Discovery Benchmarking System
Suraj Jyothi Unni, Paras Sheth, Kaize Ding +2
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evalua…
Contrastive Meta-Learning for Few-shot Node Classification
Song Wang, Zhen Tan, Huan Liu +1
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining ta…
Inductive Linear Probing for Few-shot Node Classification
Hirthik Mathavan, Zhen Tan, Nivedh Mudiam +1
Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing liter…
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…
Learning Strong Graph Neural Networks with Weak Information
Yixin Liu, Kaize Ding, Jianling Wang +3
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suf…
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…
STERLING: Synergistic Representation Learning on Bipartite Graphs
Baoyu Jing, Yuchen Yan, Kaize Ding +4
A fundamental challenge of bipartite graph representation learning is how to extract informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to address…
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding +2
Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous at…
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Yixin Liu, Kaize Ding, Huan Liu +1
Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training da…
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
Long Yu, Zhicong Luo, Huanyong Liu +3
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based…
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
Kaize Ding, Elnaz Nouri, Guoqing Zheng +2
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes…
Supervised Graph Contrastive Learning for Few-shot Node Classification
Zhen Tan, Kaize Ding, Ruocheng Guo +1
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph anno…
Few-Shot Learning on Graphs
Chuxu Zhang, Kaize Ding, Jundong Li +4
Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representat…
Physics-Informed Graph Learning
Ciyuan Peng, Feng Xia, Vidya Saikrishna +1
An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and…
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
Kaize Ding, Yancheng Wang, Yingzhen Yang +1
Graph Contrastive Learning (GCL) has recently drawn much research interest for learning generalizable node representations in a self-supervised manner. In general, the contrastive…
Data Augmentation for Deep Graph Learning: A Survey
Kaize Ding, Zhe Xu, Hanghang Tong +1
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data…
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in mo…