103 citations · 430 across the 38 of their papers we have counts for
9 papers · 2 filters
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…
Federated Few-shot Learning
Song Wang, Xingbo Fu, Kaize Ding +3
Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the…
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-…
Uncertainty-Aware Robust Learning on Noisy Graphs
Shuyi Chen, Kaize Ding, Shixiang Zhu
Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in…
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…
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
Xiongxiao Xu, Kaize Ding, Canyu Chen +1
Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The maj…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…
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…
Few-shot Node Classification with Extremely Weak Supervision
Song Wang, Yushun Dong, Kaize Ding +2
Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abund…