19 citations · 20 across the 64 of their papers we have counts for
27 papers · 1 filter
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…
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…
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…
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…
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…
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…
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
Henan Sun, Xunkai Li, Lei Zhu +4
Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation unde…
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
Enjun Du, Xunkai Li, Tian Jin +3
The era of foundation models has revolutionized AI research, yet Graph Foundation Models (GFMs) remain constrained by the scarcity of large-scale graph corpora. Traditional graph d…
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
Xunkai Li, Zhengyu Wu, Kaichi Yu +4
The directed graph (digraph), as a generalization of undirected graphs, exhibits superior representation capability in modeling complex topology systems and has garnered considerab…
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Zhengyu Wu, Boyang Pang, Xunkai Li +6
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the…
Federated Prototype Graph Learning
Zhengyu Wu, Xunkai Li, Yinlin Zhu +3
In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigat…
Rethinking Client-oriented Federated Graph Learning
Zekai Chen, Xunkai Li, Yinlin Zhu +2
As a new distributed graph learning paradigm, Federated Graph Learning (FGL) facilitates collaborative model training across local systems while preserving data privacy. We review…
Toward General and Robust LLM-enhanced Text-attributed Graph Learning
Zihao Zhang, Xunkai Li, Rong-Hua Li +3
Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LLM-enhanced TAG learning as a cri…
LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models
Zhihan Zhang, Xunkai Li, Lei Zhu +6
The emergence of large language models (LLMs) has popularized text-attributed graphs (TAGs), creating an urgent need for graph structure learning (GSL) methods that effectively lev…
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
Xiang Wu, Xunkai Li, Rong-Hua Li +2
Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, mos…
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
Xunkai Li, Bowen Fan, Zhengyu Wu +3
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in…