Search Results for author: Jeffrey Xu Yu

Found 7 papers, 2 papers with code

ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs

no code implementations17 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.

Graph Learning Language Modelling +2

A Survey of Graph Meets Large Language Model: Progress and Future Directions

1 code implementation21 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.

Language Modelling Large Language Model

Towards Feature-free TSP Solver Selection: A Deep Learning Approach

no code implementations1 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.

On Random Walk Based Graph Sampling

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.

Graph Sampling

Joint Embedding in Named Entity Linking on Sentence Level

no code implementations12 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.

Entity Linking Knowledge Graphs +1

Graph Ordering: Towards the Optimal by Learning

no code implementations18 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.

Combinatorial Optimization Community Detection +3

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