no code implementations • 26 Mar 2024 • Shuheng Fang, Kangfei Zhao, Yu Rong, ZHIXUN LI, Jeffrey Xu Yu
Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.
no code implementations • 17 Feb 2024 • Yuhan Li, Peisong Wang, ZHIXUN LI, Jeffrey Xu Yu, Jia Li
The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.
1 code implementation • 21 Nov 2023 • Yuhan Li, ZHIXUN LI, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, Jeffrey Xu Yu
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
no code implementations • 1 Jun 2020 • Kangfei Zhao, Shengcai Liu, Yu Rong, Jeffrey Xu Yu
To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.
1 code implementation • 2020 • Rong-Hua Li, Jeffrey Xu Yu, Lu Qin, Rui Mao, Tan Ji
In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.
no code implementations • 12 Feb 2020 • Wei Shi, Si-Yuan Zhang, Zhiwei Zhang, Hong Cheng, Jeffrey Xu Yu
The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.
no code implementations • 18 Jan 2020 • Kangfei Zhao, Yu Rong, Jeffrey Xu Yu, Junzhou Huang, Hao Zhang
However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.