13 citations · 31 across the 17 of their papers we have counts for
18 papers
Software World Models: From Consequence Prediction to Decision Value
Tongli Su, Yuntong Hu, Liang Zhao +3
A coding agent may safely modify one repository while silently breaking downstream services, libraries, or datastores that depend on it. Exhaustively running integration tests afte…
Towards Automatic Evolution Tree Generation from Citation Graphs
Zexing Zhao, Yuntong Hu, Liang Zhao
Surveys remain the primary way researchers grasp the lineage of methods within an AI subfield, but they scale poorly against the current rate of publication. Existing taxonomy-indu…
LARGER: Lexically Anchored Repository Graph Exploration and Retrieval
Yuntong Hu, Tongli Su, Liang Zhao +2
Repository-level coding agents must first localize the files and symbols relevant to a task; failures at this stage can cascade across downstream objectives ranging from patch gene…
RAG without Forgetting: Continual Query-Infused Key Memory
Yuntong Hu, Sha Li, Naren Ramakrishnan +1
Retrieval-augmented generation (RAG) systems commonly improve robustness via query-time adaptations such as query expansion and iterative retrieval. While effective, these approach…
Multimodal Representation Learning Conditioned on Semantic Relations
Yang Qiao, Yuntong Hu, Bowen Zhu +2
Multimodal representation learning has been largely driven by contrastive models such as CLIP, which learn a shared embedding space by aligning paired image-text samples. While eff…
Network Tomography with Path-Centric Graph Neural Network
Yuntong Hu, Junxiang Wang, Liang Zhao
Network tomography is a crucial problem in network monitoring, where the observable path performance metric values are used to infer the unobserved ones, making it essential for ta…
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs
Yuntong Hu, Zhihan Lei, Zhongjie Dai +4
Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often s…
Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories
Zheng Zhang, Hossein Amiri, Dazhou Yu +3
Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical…
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Bo Pan, Zhen Xiong, Guanchen Wu +3
Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…
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…
PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph
Dazhou Yu, Yuntong Hu, Yun Li +1
Polygon representation learning is essential for diverse applications, encompassing tasks such as shape coding, building pattern classification, and geographic question answering.…
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…
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…
Distilling Large Language Models for Text-Attributed Graph Learning
Bo Pan, Zheng Zhang, Yifei Zhang +2
Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which…
Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data
Yuntong Hu, Zheng Zhang, Liang Zhao
Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively…