257 citations · 324 across the 11 of their papers we have counts for
11 papers
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
Xiaohan Zheng, Lanning Wei, Yong Li +1
Effective decision-making on networks often relies on learning from graph-structured data, where Graph Neural Networks (GNNs) play a central role, but they take efforts to configur…
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
Lanning Wei, Jun Gao, Huan Zhao +1
Graph-structured data are the commonly used and have wide application scenarios in the real world. For these diverse applications, the vast variety of learning tasks, graph domains…
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…
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…
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…
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…
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…
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
Lanning Wei, Huan Zhao, Zhiqiang He
In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse real-world applications. To improve the model capacity, besides designing aggregation opera…
Learn Layer-wise Connections in Graph Neural Networks
Lanning Wei, Huan Zhao, Zhiqiang He
In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse applications on real-world datasets. To improve the model capacity and alleviate the over-s…
Simplifying Architecture Search for Graph Neural Network
Huan Zhao, Lanning Wei, Quanming Yao
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural archit…
Automated Machine Learning: From Principles to Practices
Zhenqian Shen, Yongqi Zhang, Lanning Wei +2
Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious. To…