19 citations · 23 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction
Juzheng Zhang, Lanning Wei, Zhen Xu +1
Link prediction is a fundamental task in graph learning, inherently shaped by the topology of the graph. While traditional heuristics are grounded in graph topology, they encounter…
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…
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…
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…
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…