791 citations · 2.5k across the 66 of their papers we have counts for
67 papers
A Versatile Graph Learning Approach through LLM-based Agent
Lanning Wei, Huan Zhao, Xiaohan Zheng +2
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve…
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Zhanke Zhou, Chenyu Zhou, Xuan Li +3
Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the f…
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Jianing Zhu, Jiangchao Yao, Tongliang Liu +3
Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adve…
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
Lanning Wei, Zhiqiang He, Huan Zhao +1
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing probl…
Improving Zero-Shot Coordination Performance Based on Policy Similarity
Lebin Yu, Yunbo Qiu, Quanming Yao +2
Over these years, multi-agent reinforcement learning has achieved remarkable performance in multi-agent planning and scheduling tasks. It typically follows the self-play setting, w…
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach
Lanning Wei, Zhiqiang He, Huan Zhao +1
In recent years, Graph Neural Networks (GNNs) have been popular in graph representation learning which assumes the homophily property, i.e., the connected nodes have the same label…
Search to Pass Messages for Temporal Knowledge Graph Completion
Zhen Wang, Haotong Du, Quanming Yao +1
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topologica…
Searching a High-Performance Feature Extractor for Text Recognition Network
Hui Zhang, Quanming Yao, James T. Kwok +1
Feature extractor plays a critical role in text recognition (TR), but customizing its architecture is relatively less explored due to expensive manual tweaking. In this work, inspi…
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
Yinfeng Li, Chen Gao, Quanming Yao +3
Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Exist…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on differe…
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Xu Wang, Huan Zhao, Lanning Wei +1
Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classific…
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han +1
Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Huiming Chen, Huandong Wang, Quanming Yao +3
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The prima…
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Yaqing Wang, Song Wang, Quanming Yao +1
Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we pr…
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
Zhen Xu, Sergio Escalera, Isabelle Guyon +5
Obtaining standardized crowdsourced benchmark of computational methods is a major issue in data science communities. Dedicated frameworks enabling fair benchmarking in a unified en…
Pooling Architecture Search for Graph Classification
Lanning Wei, Huan Zhao, Quanming Yao +1
Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the…
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Xiawei Guo, Yuhan Quan, Huan Zhao +3
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing wo…
Knowledge Graph Reasoning with Relational Digraph
Yongqi Zhang, Quanming Yao
Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning…
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao +1
Molecular property prediction plays a fundamental role in drug discovery to identify candidate molecules with target properties. However, molecular property prediction is essential…
Bilinear Scoring Function Search for Knowledge Graph Learning
Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been…
Efficient Data-specific Model Search for Collaborative Filtering
Chen Gao, Quanming Yao, Depeng Jin +1
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences…
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
Shimin Di, Quanming Yao, Yongqi Zhang +1
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also…