10 citations · 19 across the 10 of their papers we have counts for
10 papers
Taxonomy Tree Generation from Citation Graph
Yuntong Hu, Zhuofeng Li, Zheng Zhang +4
Constructing taxonomies from citation graphs is essential for organizing scientific knowledge, facilitating literature reviews, and identifying emerging research trends. However, m…
TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
Zhuofeng Li, Zixing Gou, Xiangnan Zhang +6
Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-w…
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Zheng Zhang, Yuntong Hu, Bo Pan +2
Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum…
Link Prediction on Textual Edge Graphs
Chen Ling, Zhuofeng Li, Yuntong Hu +5
Textual-edge Graphs (TEGs), characterized by rich text annotations on edges, are increasingly significant in network science due to their ability to capture rich contextual informa…
GRAG: Graph Retrieval-Augmented Generation
Yuntong Hu, Zhihan Lei, Zheng Zhang +3
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in…
Source Localization for Cross Network Information Diffusion
Chen Ling, Tanmoy Chowdhury, Jie Ji +3
Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing metho…
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Nguyen Do, Tanmoy Chowdhury, Chen Ling +2
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one…
ELAD: Explanation-Guided Large Language Models Active Distillation
Yifei Zhang, Bo Pan, Chen Ling +2
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional di…
SparseLLM: Towards Global Pruning for Pre-trained Language Models
Guangji Bai, Yijiang Li, Chen Ling +2
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has eme…
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models
Mingchen Li, Chen Ling, Rui Zhang +1
Zero-shot link prediction (ZSLP) on knowledge graphs aims at automatically identifying relations between given entities. Existing methods primarily employ auxiliary information to…