55 citations · 84 across the 15 of their papers we have counts for
17 papers
Harness-G: A Graph-Structured Harness for Search Agents
Yanning Hou, Haoyuan Chen, Sihang Zhou +7
Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. C…
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
Xirui Liu, Sihang Zhou, Yanning Hou +6
Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowled…
AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation
Yanning Hou, Duanyang Yuan, Sihang Zhou +5
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and…
Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
Jiaxin Zhang, Yiqi Wang, Siwei Wang +4
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, e…
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
Meng Liu, Ke Liang, Siwei Wang +3
Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the…
Parameter-Free Clustering via Self-Supervised Consensus Maximization (Extended Version)
Lijun Zhang, Suyuan Liu, Siwei Wang +4
Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, lim…
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Xihong Yang, Siwei Wang, Jiaqi Jin +6
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, whe…
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
Xihong Yang, Siwei Wang, Fangdi Wang +6
Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source info…
Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
Yi Wen, Yue Liu, Derong Xu +9
Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great su…
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Yuzhuo Dai, Jiaqi Jin, Zhibin Dong +6
In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-vie…
Soft Reasoning Paths for Knowledge Graph Completion
Yanning Hou, Sihang Zhou, Ke Liang +6
Reasoning paths are reliable information in knowledge graph completion (KGC) in which algorithms can find strong clues of the actual relation between entities. However, in real-wor…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
Jiaqi Jin, Siwei Wang, Zhibin Dong +4
Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors lik…
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection
Siqi Wang, Yuanze Hu, Xinwang Liu +5
Industrial image anomaly detection (IAD) is a pivotal topic with huge value. Due to anomaly's nature, real anomalies in a specific modern industrial domain (i.e. domain-specific an…
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
Dayu Hu, Zhibin Dong, Ke Liang +3
Single-cell multi-view clustering enables the exploration of cellular heterogeneity within the same cell from different views. Despite the development of several multi-view cluster…
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
Qiyuan Ou, Siwei Wang, Pei Zhang +2
Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up til…
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
Qun Zheng, Xihong Yang, Siwei Wang +2
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. M…