103 citations · 360 across the 124 of their papers we have counts for
38 papers · 1 filter
A Computationally Efficient Framework for Overlapping Community Detection in Large Bipartite Graphs
Yue Zeng, Rong-Hua Li, Qiangqiang Dai +1
Community detection, which uncovers closely connected vertex groups in networks, is vital for applications in social networks, recommendation systems, and beyond. Real-world networ…
δ-EMG: A Monotonic Graph Index for Approximate Nearest Neighbor Search
Liming Xiang, Jing Feng, Ziqi Yin +5
Approximate nearest neighbor (ANN) search in high-dimensional spaces is a foundational component of many modern retrieval and recommendation systems. Currently, almost all algorith…
Scalable and Provable Kemeny Constant Computation on Static and Dynamic Graphs: A 2-Forest Sampling Approach
Cheng Li, Meihao Liao, Rong-Hua Li +1
Kemeny constant, defined as the expected hitting time of random walks from a source node to a randomly chosen target node, is a fundamental metric in graph data management with man…
Unstructured Data Analysis using LLMs: A Comprehensive Benchmark
Qiyan Deng, Jianhui Li, Chengliang Chai +9
Nowadays, the explosion of unstructured data presents immense analytical value. Leveraging the remarkable capability of large language models (LLMs) in extracting attributes of str…
Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
Bowen Fan, Zhilin Guo, Xunkai Li +5
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular che…
MagicDock: Toward Docking-oriented De Novo Ligand Design via Gradient Inversion
Zekai Chen, Xunkai Li, Sirui Zhang +6
De novo ligand design is a fundamental task that seeks to generate protein or molecule candidates that can effectively dock with protein receptors and achieve strong binding affini…
FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
Zhengyu Wu, Yinlin Zhu, Xunkai Li +4
Foundation models have shown remarkable cross-domain generalization in language and vision, inspiring the development of graph foundation models (GFMs). However, existing GFMs typi…
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
Zhengyu Wu, Daohan Su, Yang Zhang +3
Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information. However, existing methods…
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
Zhihan Zhang, Xunkai Li, Yilong Zuo +5
Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantic…
Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Xunkai Li, Daohan Su, Sicheng Liu +5
Inspired by the success of LLMs, GFMs are designed to learn the optimal embedding functions from multi-domain text-attributed graphs for the downstream cross-task generalization ca…
State Space Models over Directed Graphs
Junzhi She, Xunkai Li, Rong-Hua Li +1
Directed graphs are ubiquitous across numerous domains, where the directionality of edges encodes critical causal dependencies. However, existing GNNs and graph Transformers tailor…
Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: a Labelling Approach
Meihao Liao, Yueyang Pan, Rong-Hua Li +1
Resistance distance computation is a fundamental problem in graph analysis, yet existing random walk-based methods are limited to approximate solutions and suffer from poor efficie…
Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets
Lianpeng Qiao, Ziqi Cao, Kaiyu Feng +2
Data has become a foundational asset driving innovation across domains such as finance, healthcare, and e-commerce. In these areas, predictive modeling over relational tables is co…
Optimal -Dense Subgraph Search in Bipartite Graphs
Yalong Zhang, Rong-Hua Li, Qi Zhang +1
Dense subgraph search in bipartite graphs is a fundamental problem in graph analysis, with wide-ranging applications in fraud detection, recommendation systems, and social network…
DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations
Yuhan Guo, Lizhong Ding, Shihan Jia +6
Explainable AI (XAI) builds trust in complex systems through model attribution methods that reveal the decision rationale. However, due to the absence of a unified optimal explanat…
Federated Graph Unlearning
Yuming Ai, Xunkai Li, Jiaqi Chao +5
The demand for data privacy has led to the development of frameworks like Federated Graph Learning (FGL), which facilitate decentralized model training. However, a significant oper…
LAVA: Language Driven Scalable and Versatile Traffic Video Analytics
Yanrui Yu, Tianfei Zhou, Jiaxin Sun +4
In modern urban environments, camera networks generate massive amounts of operational footage -- reaching petabytes each day -- making scalable video analytics essential for effici…
HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization
Yanheng Hou, Xunkai Li, Zhenjun Li +3
In recent years, Hypergraph Neural Networks (HNNs) have demonstrated immense potential in handling complex systems with high-order interactions. However, acquiring large-scale, hig…
A Comprehensive Data-centric Overview of Federated Graph Learning
Zhengyu Wu, Xunkai Li, Yinlin Zhu +8
In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence b…
QUEST: Query Optimization in Unstructured Document Analysis
Zhaoze Sun, Qiyan Deng, Chengliang Chai +6
Most recently, researchers have started building large language models (LLMs) powered data systems that allow users to analyze unstructured text documents like working with a datab…
Improved Algorithms for Effective Resistance Computation on Graphs
Yichun Yang, Rong-Hua Li, Meihao Liao +1
Effective Resistance (ER) is a fundamental tool in various graph learning tasks. In this paper, we address the problem of efficiently approximating ER on a graph $\mathcal{G}=(\mat…
On the Efficient Discovery of Maximum -Defective Biclique
Donghang Cui, Ronghua Li, Qiangqiang Dai +2
The problem of identifying the maximum edge biclique in bipartite graphs has attracted considerable attention in bipartite graph analysis, with numerous real-world applications suc…
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
Yanzhe Wen, Xunkai Li, Qi Zhang +4
Recently, large language models (LLMs) have significantly advanced text-attributed graph (TAG) learning. However, existing methods inadequately handle data uncertainty in open-worl…
Not All Documents Are What You Need for Extracting Instruction Tuning Data
Chi Zhang, Huaping Zhong, Hongtao Li +11
Instruction tuning improves the performance of large language models (LLMs), but it heavily relies on high-quality training data. Recently, LLMs have been used to synthesize instru…