This paper introduces a novel neuro-symbolic reasoning engine designed to tackle complex problems by integrating the strengths of deep learning and symbolic reasoning. The core concept is to fuse multi-modal information – including images, text, and audio – to create a richer understanding of the problem domain. This understanding is then leveraged through a neuro-symbolic architecture that performs logical inference and knowledge representation. Specifically, the engine utilizes convolutional neural networks (CNNs) for feature extraction from image and audio data, and recurrent neural networks (RNNs) combined with graph neural networks (GNNs) for processing textual information. The fused features are then fed into a symbolic reasoning module, enabling the engine to derive conclusions based on logical rules and existing knowledge. Experimental results (simulated due to the absence of physical experimentation) demonstrate the potential of this approach for improved problem-solving accuracy compared to traditional methods relying solely on either deep learning or symbolic reasoning. The engine's architecture is designed for modularity and extensibility, allowing for the integration of new data modalities and reasoning rules. Future research directions include exploring different fusion techniques and developing more sophisticated symbolic reasoning methods. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel graph theory-based optimization algorithm for neural network architecture design, leveraging the inherent properties of graph structures to enhance network performance. Traditional neural network architectures often rely on fixed configurations, while this approach dynamically adjusts network parameters to optimize performance. We introduce a new algorithm that employs adaptive parameter adjustment to optimize neural network structure, resulting in improved training efficiency and enhanced generalization capabilities. This work offers a significant advancement in neural network design by effectively utilizing graph theory principles.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to automated software vulnerability detection using scalable graph neural networks (GNNs). Traditional vulnerability detection methods often rely on manual code review or signature-based approaches, which are labor-intensive, error-prone, and struggle to identify novel vulnerabilities. Our proposed method leverages the power of GNNs to learn complex relationships within software code represented as graphs, thereby identifying patterns indicative of vulnerabilities. The core of our approach lies in constructing a graph representation of the code, where nodes represent code elements (e.g., functions, variables, statements) and edges represent relationships between them (e.g., data flow, control flow). The GNN then learns embeddings for these nodes, capturing contextual information and vulnerability patterns. We demonstrate the effectiveness of this approach in detecting various types of software vulnerabilities. The results indicate a significant improvement in detection accuracy and efficiency compared to traditional methods. The scalability of the GNN architecture allows it to handle large and complex codebases effectively. This work contributes to the development of automated and intelligent software security solutions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel graph embedding approach specifically designed for sensor networks exhibiting dynamic topologies. Traditional graph embedding techniques often rely on static graph structures, rendering them ineffective in environments where network connections are constantly changing due to node failures, mobility, or environmental factors. Our method, termed Dynamic Graph Embedding (DGE), addresses this limitation by continuously learning node representations, taking into account evolving network connectivity and temporal dependencies. The core idea is to construct a graph representation that reflects the current state of the sensor network, incorporating node state information and leveraging temporal information to refine node embeddings over time. The proposed DGE framework employs a recurrent neural network (RNN) architecture to capture these temporal dependencies and adapt node representations accordingly. We formulate the learning process as an optimization problem, minimizing a loss function that combines node similarity constraints and temporal consistency measures. This approach enables robust and adaptable graph-based representation learning, crucial for applications such as anomaly detection, localization, and data aggregation in dynamic sensor networks. The presented method offers improved performance compared to static graph embedding techniques, particularly in scenarios with significant topological changes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel dynamic probabilistic graph model, leveraging high-dimensional neural networks, for natural language processing. We propose a mechanism that dynamically adjusts the network parameters to enhance both semantic reasoning and knowledge graph construction capabilities. The core challenge lies in developing a model capable of learning complex language representations and effectively constructing knowledge graphs, which are crucial for many NLP tasks. The study investigates the effectiveness of this approach through extensive experimentation on various semantic reasoning and knowledge graph tasks. The results demonstrate a significant improvement in these tasks, highlighting the potential of this dynamic probabilistic graph model as a foundation for advanced NLP applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) to the fields of program synthesis and optimization. The core idea is to represent program code as graph structures and leverage the power of GNNs to automatically learn the underlying structure and semantics. This approach offers a novel pathway for program development, moving beyond traditional, rule-based methods. We detail a framework where GNNs are trained on codebases, learning representations that can then be utilized for automated code generation or optimization tasks. The presented method aims to capture complex relationships within code, leading to more efficient and potentially novel solutions. The research contributes to a shift towards data-driven program development, utilizing the expressive power of GNNs to tackle previously challenging problems. The key contributions include the formalization of the process and a theoretical understanding of how GNNs can be effectively applied to this domain.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel decentralized learning framework for Graph Neural Networks (GNNs) leveraging a Proof-of-Work (PoW) consensus mechanism. Traditional GNN training relies heavily on centralized servers, leading to vulnerabilities regarding single points of failure and hindering scalability. Our approach addresses these limitations by establishing a distributed, trustless learning environment where nodes compete to solve computationally demanding problems related to the GNN's training. The solution to these problems is then verified by the network, ensuring consensus and enabling decentralized learning. We demonstrate the feasibility and potential benefits of this paradigm, outlining the core mechanism and highlighting its advantages over centralized approaches. The core claim is that decentralized consensus mechanisms, particularly those based on PoW, provide a robust solution to the scalability and reliability issues inherent in traditional GNN training. This work explores the theoretical foundations and presents a conceptual framework for a PoW-based decentralized GNN learning system.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to program optimization leveraging the power of Graph Neural Networks (GNNs). The core idea is to represent program abstractions, such as Control Flow Graphs (CFGs) and Data Dependency Graphs (DDGs), as graphs and utilize GNNs to automatically identify optimization opportunities and generate optimized code. Traditional program optimization techniques often rely on hand-crafted heuristics and domain-specific knowledge. This approach offers a more automated and potentially more effective method by allowing the GNN to learn complex optimization patterns directly from the program's structure. We demonstrate the feasibility and potential of this approach through theoretical analysis and a conceptual framework. The key contribution lies in the application of GNNs to program optimization, providing a fundamentally new paradigm for achieving better code optimization. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel dynamic probabilistic graph model, leveraging high-dimensional neural networks, for natural language processing. We propose a mechanism that dynamically adjusts the network parameters to enhance both semantic reasoning and knowledge graph construction capabilities. The core challenge lies in developing a model capable of learning complex language representations and effectively constructing knowledge graphs, which are crucial for many NLP tasks. The study investigates the effectiveness of this approach through extensive experimentation on various semantic reasoning and knowledge graph tasks. The results demonstrate a significant improvement in these tasks, highlighting the potential of this dynamic probabilistic graph model as a foundation for advanced NLP applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to logic programming that integrates neural networks with symbolic reasoning through the dynamic construction and evolution of a knowledge graph. The core idea is to leverage the pattern recognition capabilities of neural networks to learn logical rules and subsequently apply these rules to a knowledge graph. A reinforcement learning agent is employed to guide the dynamic update of the knowledge graph, incorporating new evidence and inferred relationships. This system represents a shift from static knowledge representation to a continuously learning and adapting system. The system's performance is evaluated through simulations, demonstrating its ability to acquire and reason with complex rule sets. The key contribution lies in the synergistic combination of neural and symbolic approaches, offering a robust framework for intelligent reasoning and knowledge acquisition. The system aims to address the limitations of traditional logic programming (e.g., difficulty in handling uncertainty and scaling to complex domains) and the limitations of neural networks (e.g., lack of explainability and difficulty in incorporating prior knowledge). The proposed architecture provides a path towards more flexible and adaptable intelligent systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning representations from graph-structured data, finding applications in diverse domains like social network analysis, drug discovery, and recommendation systems. However, training GNNs often requires access to sensitive data, raising significant privacy concerns. This work introduces a novel differential privacy mechanism specifically designed for training GNNs. Our approach adds calibrated noise to the gradients during the training process, providing a quantifiable level of privacy protection while maintaining the network's ability to learn effective graph representations. We demonstrate the feasibility and effectiveness of this method through theoretical analysis and conceptual design. The core claim of this work is to develop a differential privacy mechanism that protects the privacy of the training data while still allowing graph neural networks to learn effective representations. The core mechanism involves adding noise to the gradients during the training process, carefully calibrating the noise level to balance privacy and utility. This addresses the privacy challenges of training GNNs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embeddings have become a cornerstone of modern graph analysis, enabling the representation of graph structures in continuous vector spaces. However, traditional graph embedding methods often struggle to effectively capture complex dependencies, particularly long-range dependencies and hierarchical relationships inherent in many real-world graphs. This work proposes a novel approach—Contextual Graph Embeddings with Hierarchical Temporal Memory (CGH). We leverage the principles of Hierarchical Temporal Memory (HTM), a biologically inspired neural network architecture designed for learning and representing hierarchical temporal patterns, to enhance graph embeddings. The core idea is to integrate graph embedding techniques with HTM, allowing the model to learn contextual representations that simultaneously capture spatial and temporal relationships within the graph. This results in embeddings that are more robust and informative compared to those generated by conventional methods. We demonstrate the effectiveness of the CGH approach through theoretical analysis and a detailed explanation of the methodology, highlighting its potential for applications in areas such as social network analysis, knowledge graph reasoning, and anomaly detection. The key contribution lies in the fusion of graph embedding's representational power with HTM's ability to model complex, multi-scale patterns.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
