The field of graph neural networks and quantum computing is rapidly evolving, with a focus on improving the efficiency and effectiveness of various applications. Recent research has explored the use of large language models and graph neural networks to enhance data efficiency in graph out-of-distribution detection, as well as the development of novel frameworks for graph-based personality detection and anomaly detection in microservice applications. Additionally, there have been significant advancements in quantum computing, including the development of high-performance software-hardware collaborative frameworks and hybrid quantum vision transformers for image classification. Noteworthy papers include LLM-GOOD, which proposes a general framework for combining the strengths of large language models and graph neural networks, and QPanda3, a high-performance quantum programming framework that enhances quantum computing efficiency through optimized circuit compilation and hardware-aware execution strategies.
Advances in Graph Neural Networks and Quantum Computing
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Few-Shot Graph Out-of-Distribution Detection with LLMs
CFiCS: Graph-Based Classification of Common Factors and Microcounseling Skills
detectGNN: Harnessing Graph Neural Networks for Enhanced Fraud Detection in Credit Card Transactions
On the difficulty of order constrained pattern matching with applications to feature matching based malware detection
ObfusQate: Unveiling the First Quantum Program Obfuscation Framework
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
Quantum Methods for Managing Ambiguity in Natural Language Processing
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
Effect-driven interpretation: Functors for natural language composition
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
S3C2 Summit 2024-08: Government Secure Supply Chain Summit
LimTDD: A Compact Decision Diagram Integrating Tensor and Local Invertible Map Representations
Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code
Refining Interactions: Enhancing Anisotropy in Graph Neural Networks with Language Semantics
Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights
From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow
Distributed Temporal Graph Learning with Provenance for APT Detection in Supply Chains
Toward General and Robust LLM-enhanced Text-attributed Graph Learning
QPanda3: A High-Performance Software-Hardware Collaborative Framework for Large-Scale Quantum-Classical Computing Integration
HQViT: Hybrid Quantum Vision Transformer for Image Classification