48 citations · 299 across the 31 of their papers we have counts for
13 papers · 1 filter
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
Jingcan Duan, Siwei Wang, Pei Zhang +5
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture ano…
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
Ke Liang, Lingyuan Meng, Meng Liu +6
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research dir…
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
Xihong Yang, Erxue Min, Ke Liang +6
Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. The quality of contrastive samples is crucial for achie…
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
Jingcan Duan, Bin Xiao, Siwei Wang +2
Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anom…
Regret Analysis for Hierarchical Experts Bandit Problem
Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer…
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization
Siwei Wang, Xinwang Liu, En Zhu
Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in v…
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +3
In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size in the regret bound, where is the total number of arms…
Multiple Kernel Clustering with Dual Noise Minimization
Junpu Zhang, Liang Li, Siwei Wang +4
Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating comple…
Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph
Liang Li, Siwei Wang, Xinwang Liu +4
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of…
Simple Contrastive Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +1
Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming g…
Thompson Sampling for (Combinatorial) Pure Exploration
Siwei Wang, Jun Zhu
Existing methods of combinatorial pure exploration mainly focus on the UCB approach. To make the algorithm efficient, they usually use the sum of upper confidence bounds within arm…
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each ste…
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
Siwei Wang, Xinwang Liu, Suyuan Liu +4
Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied…