103 citations · 167 across the 19 of their papers we have counts for
20 papers
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…
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…
Efficient Maximum Fair Clique Search over Large Networks
Qi Zhang, Rong-Hua Li, Zifan Zheng +3
Mining cohesive subgraphs in attributed graphs is an essential problem in the domain of graph data analysis. The integration of fairness considerations significantly fuels interest…
Learning to Generate Parameters of ConvNets for Unseen Image Data
Shiye Wang, Kaituo Feng, Changsheng Li +2
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn networ…
DREAM: Domain-free 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 (, the number of convolutional layers) of…
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Kaituo Feng, Yikun Miao, Changsheng Li +2
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper te…
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Hanjie Li, Changsheng Li, Kaituo Feng +3
Graph structured data often possess dynamic characters in nature. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph da…
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Kaituo Feng, Changsheng Li, Ye Yuan +1
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teache…
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Shiye Wang, Changsheng Li, Yanming Li +2
In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck…
Deep Unsupervised Active Learning on Learnable Graphs
Handong Ma, Changsheng Li, Xinchu Shi +2
Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while…
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
Yanming Li, Changsheng Li, Shiye Wang +2
Deep subspace clustering has attracted increasing attention in recent years. Almost all the existing works are required to load the whole training data into one batch for learning…
Causal Effect Estimation using Variational Information Bottleneck
Zhenyu Lu, Yurong Cheng, Mingjun Zhong +3
Causal inference is to estimate the causal effect in a causal relationship when intervention is applied. Precisely, in a causal model with binary interventions, i.e., control and t…
Efficient Top-k Ego-Betweenness Search
Qi Zhang, Rong-Hua Li, Minjia Pan +3
Betweenness centrality, measured by the number of times a vertex occurs on all shortest paths of a graph, has been recognized as a key indicator for the importance of a vertex in t…
Multi-attributed Community Search in Road-social Networks
Fangda Guo, Ye Yuan, Guoren Wang +2
Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user pref…
On Deep Unsupervised Active Learning
Changsheng Li, Handong Ma, Zhao Kang +3
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.…
Scaling Up Distance-generalized Core Decomposition
Qiangqiang Dai, Rong-Hua Li, Lu Qin +4
Core decomposition is a fundamental operator in network analysis. In this paper, we study the problem of computing distance-generalized core decomposition on a network. A distance-…
Cracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index
Gang Wu, Yidong Song, Guodong Zhao +5
Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also lead…
Mining Bursting Communities in Temporal Graphs
Hongchao Qin, Rong-Hua Li, Guoren Wang +3
Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential to seek emergency events in temporal graphs. Unfortunately, most previous stud…
A Comparative Study of Consistent Snapshot Algorithms for Main-Memory Database Systems
Liang Li, Guoren Wang, Gang Wu +3
In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have ef…
Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases
Ye Yuan, Guoren Wang, Lei Chen +1
Many studies have been conducted on seeking the efficient solution for subgraph similarity search over certain (deterministic) graphs due to its wide application in many fields, in…