103 citations · 360 across the 124 of their papers we have counts for
28 papers · 1 filter
Fast Biclique Counting on Bipartite Graphs: A Node Pivot-based Approach
Xiaowei Ye, Rong-Hua Li, Longlong Lin +2
Counting the number of -bicliques (complete bipartite subgraphs) in a bipartite graph is a fundamental problem which plays a crucial role in numerous bipartite graph analys…
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu +4
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration…
Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach
Yichun Yang, Rong-Hua Li, Meihao Liao +2
Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting various applications such as graph node similarity queries, graph node ranking, graph clu…
Towards Data-centric Machine Learning on Directed Graphs: a Survey
Henan Sun, Xunkai Li, Daohan Su +3
In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which…
ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving
Rongqing Li, Changsheng Li, Yuhang Li +5
Trajectory prediction of agents is crucial for the safety of autonomous vehicles, whereas previous approaches usually rely on sufficiently long-observed trajectory to predict the f…
CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution
Rui Li, Kangfei Zhao, Jeffrey Xu Yu +1
Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches stru…
DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model
Rongqing Li, Jiaqi Yu, Changsheng Li +3
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a…
Multivariate Time Series Cleaning under Speed Constraints
Aoqian Zhang, Zexue Wu, Yifeng Gong +2
Errors are common in time series due to unreliable sensor measurements. Existing methods focus on univariate data but do not utilize the correlation between dimensions. Cleaning ea…
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
Chi Zhang, Huaping Zhong, Kuan Zhang +10
Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, re…
DiRW: Path-Aware Digraph Learning for Heterophily
Daohan Su, Xunkai Li, Zhenjun Li +3
Recently, graph neural network (GNN) has emerged as a powerful representation learning tool for graph-structured data. However, most approaches are tailored for undirected graphs,…
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
Xi Chen, Kaituo Feng, Changsheng Li +4
Low-rank training has emerged as a promising approach for reducing memory usage in training Large Language Models (LLMs). Previous methods either rely on decomposing weight matrice…
Macformer: Transformer with Random Maclaurin Feature Attention
Yuhan Guo, Lizhong Ding, Ye Yuan +1
Random feature attention (RFA) adopts random fourier feature (RFF) methods to approximate the softmax function, resulting in a linear time and space attention mechanism that enable…
HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data
Guozheng Li, Haotian Mi, Chi Harold Liu +2
When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effectiv…
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Xunkai Li, Yinlin Zhu, Boyang Pang +7
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inher…
Efficient Antagonistic k-plex Enumeration in Signed Graphs
Lantian Xu, Rong-Hua Li, Dong Wen +3
A signed graph is a graph where each edge receives a sign, positive or negative. The signed graph model has been used in many real applications, such as protein complex discovery a…
Influence Maximization in Hypergraphs by Stratified Sampling for Efficient Generation of Reverse Reachable Sets
Lingling Zhang, Hong Jiang, Ye Yuan +1
Given a hypergraph, influence maximization (IM) is to discover a seed set containing vertices that have the maximal influence. Although the existing vertex-based IM algorithms…
InsigHTable: Insight-driven Hierarchical Table Visualization with Reinforcement Learning
Guozheng Li, Peng He, Xinyu Wang +5
Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users' attention. The created hierarchical…
Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
Kaituo Feng, Changsheng Li, Xiaolu Zhang +3
Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically r…
Counting Cohesive Subgraphs with Hereditary Properties
Rong-Hua Li, Xiaowei Ye, Fusheng Jin +3
Counting small cohesive subgraphs in a graph is a fundamental operation with numerous applications in graph analysis. Previous studies on cohesive subgraph counting are mainly base…
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
Kaituo Feng, Changsheng Li, Dongchun Ren +2
End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neural networks render…
Scalable -clique Densest Subgraph Search
Xiaowei Ye, Miao Qiao, Rong-Hua Li +2
In this paper, we present a collection of novel and scalable algorithms designed to tackle the challenges inherent in the -clique densest subgraph problem (\kcdsp) within networ…
K-stars LDP: A Novel Framework for (p, q)-clique Enumeration under Local Differential Privacy
Henan Sun, Zhengyu Wu, Rong-Hua Li +2
(p,q)-clique enumeration on a bipartite graph is critical for calculating clustering coefficient and detecting densest subgraph. It is necessary to carry out subgraph enumeration w…
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xunkai Li, Meihao Liao, Zhengyu Wu +4
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…