Jiujiang Guo

Orcid: 0000-0001-8614-4643

According to our database1, Jiujiang Guo authored at least 25 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
FNES: Formulating Natural World Rules via Equiangular Spirals to Strengthen Temporal Knowledge Representation.
IEEE Trans. Knowl. Data Eng., June, 2026

Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability.
CoRR, May, 2026

From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG.
CoRR, May, 2026

Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks.
CoRR, April, 2026

Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systems.
CoRR, March, 2026

CoDA: Exploring Chain-of-Distribution Attacks and Post-Hoc Token-Space Repair for Medical Vision-Language Models.
CoRR, March, 2026

A Semantic Decoupling-Based Two-Stage Rainy-Day Attack for Revealing Weather Robustness Deficiencies in Vision-Language Models.
CoRR, January, 2026

From co-occurrence to coherence: Quantum-informed representation learning for knowledge graph completion.
Knowl. Based Syst., 2026

SFPL: Sensitivity feature perception learning based on stochastic differential equations for temporal knowledge graph completion.
Knowl. Based Syst., 2026

LieT- H<sup>2</sup>K: Temporal Homogeneous and Heterogeneous Knowledge Joint Representation Driven by Lie Group.
Proceedings of the Database Systems for Advanced Applications, 2026

Knowledge Graph Completion via Centroid-Driven Adaptive Type Information Capture Graph Attention Network.
Proceedings of the Database Systems for Advanced Applications, 2026

2025
Multi-perspective semantic decoupling and enhancement in graph attention network for knowledge graph completion.
Appl. Intell., May, 2025

EHPR: Learning evolutionary hierarchy perception representation based on quaternion for temporal knowledge graph completion.
Inf. Sci., 2025

TeDS: Joint Learning of Diachronic and Synchronic Perspectives in Quaternion Space for Temporal Knowledge Graph Completion.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

MTE: Multi Transformation of Entities in Quaternion Vector Space for Temporal Knowledge Graph Completion.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

A Temporal Knowledge Completion Model Driven by Dual-Module for Evolutionary Feature Perception.
Proceedings of the Web Information Systems and Applications, 2025

2024
TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion.
Knowl. Based Syst., 2024

Hierarchical Hyperedge Graph Transformer: Toward Dynamic Interactions of Brain Networks for Neurodevelopmental Disease Diagnosis.
IEEE Access, 2024

Graph Attention Network with Relational Dynamic Factual Fusion for Knowledge Graph Completion.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

AttFGCN: A GCN-Based Method Using Attention Flow for Knowledge Graph Completion.
Proceedings of the Database Systems for Advanced Applications, 2024

2023
SEPAKE: a structure-enhanced and position-aware knowledge embedding framework for knowledge graph completion.
Appl. Intell., October, 2023

BDRI: block decomposition based on relational interaction for knowledge graph completion.
Data Min. Knowl. Discov., March, 2023

TBDRI: block decomposition based on relational interaction for temporal knowledge graph completion.
Appl. Intell., March, 2023

Combination of Translation and Rotation in Dual Quaternion Space for Temporal Knowledge Graph Completion.
Proceedings of the International Joint Conference on Neural Networks, 2023


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