This paper presents a novel approach to complex system modeling and simulation utilizing graph neural networks (GNNs). The core focus is to develop a method capable of automatically learning complex relationships within a system, and generating high-fidelity predictions. Traditional methods often struggle to effectively model dynamic relationships. Graph Neural Networks (GNNs) offer a powerful tool for this purpose. This paper details the architecture of our proposed GNN-based system, its training methodology, and its demonstrated performance in simulating a complex system. We provide a comprehensive analysis of the method's strengths and weaknesses, highlighting its potential for advancing the field of complex system modeling and simulation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive graph neural network (GNN) architecture based on dynamic graph representations. Traditional GNNs often struggle with the inherent limitations of static graphs, hindering their ability to effectively handle complex and dynamic data. Our proposed method leverages a dynamic graph structure, enabling the network to adapt to evolving data patterns and improve model generalization. We detail a mechanism for dynamically updating the graph's structure based on incoming data, facilitating a more responsive and robust learning process. The design incorporates a novel update rule that ensures a balance between adaptation and stability, resulting in enhanced performance across a range of datasets. We demonstrate the effectiveness of our approach through extensive experiments on several benchmark datasets, showcasing significant improvements in accuracy and generalization compared to existing GNN methods. This work establishes a foundation for more adaptive and robust GNN models, particularly suitable for applications involving data with significant temporal or structural changes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future graph states given their past states, utilizing a contrastive loss function to promote similarity between embeddings of structurally similar graphs. This approach offers a more principled and stable learning process compared to existing methods. We demonstrate the effectiveness of our approach through a theoretical analysis and outline the key components and operational details. The primary contribution lies in establishing a clear connection between predictive modeling and graph embedding learning, providing a foundation for future research in this area. The proposed method aims to generate embeddings that capture the underlying structural information of graphs effectively, ultimately improving performance in downstream graph-related tasks. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel method for complex system modeling based on graph neural networks (GNNs) and adaptive learning. Traditional approaches often require manual design of complex network structures, limiting the model's flexibility. Our method leverages GNNs to automatically learn and update graph structures, enabling more accurate representation of system-internal relationships. We propose an adaptive learning algorithm that iteratively refines the graph structure based on data observations, leading to improved model performance. The core mechanism involves utilizing GNNs to capture the intricate dependencies within a system, while the adaptive learning ensures the model continuously adjusts its representation to better reflect the underlying dynamics. The proposed approach offers a significant advancement in the modeling of complex systems by providing a flexible and automated framework for graph structure learning and dynamic representation refinement.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper addresses the limitations of current deep learning models in performing abstract symbolic reasoning. The core argument is that deep learning's reliance on pattern recognition hinders its ability to grasp underlying concepts and relationships, leading to a lack of genuine understanding. We propose a novel hierarchical symbolic computation framework designed to overcome these deficiencies. This framework combines the pattern recognition capabilities of deep neural networks with a hierarchical symbolic representation of knowledge. The neural network is tasked with generating symbolic representations from raw input, which are then fed into a symbolic inference engine operating on a hierarchical knowledge graph. This architecture allows for reasoning at multiple levels of abstraction, mimicking the way humans solve complex problems. The key innovation lies in the synergistic integration of these two approaches, fostering a system capable of both learning representations and performing logical deductions. The system's ability to handle ambiguity and context is significantly enhanced through the structured, hierarchical knowledge graph. We outline the system architecture, the training methodology, and the inference process, highlighting the benefits of this hybrid approach. Ultimately, this research contributes to a more robust and flexible approach to artificial intelligence, particularly in domains requiring sophisticated abstract reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel neuro-symbolic reasoning framework centered around the dynamic construction of a knowledge graph. The core challenge in integrating neural networks and symbolic reasoning lies in the inherent differences in their representations and the difficulty in seamless translation between them. Our approach addresses this by establishing a continuous feedback loop. A neural network generates initial hypotheses, which are then used to construct a knowledge graph. Symbolic rules are then applied to refine and constrain this graph, ensuring consistency and logical validity. This dynamic process allows for a more robust and interpretable knowledge representation, moving beyond the limitations of static knowledge graphs. We detail the architecture, the ruleset, and the interaction mechanisms, emphasizing the system's ability to adapt and learn from both neural and symbolic sources. The system's performance is evaluated through a series of reasoning tasks, demonstrating its effectiveness in complex scenarios.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel graph neural network (GNN) optimization algorithm based on a dynamically adjusted attention mechanism. Traditional GNN optimization methods often rely on fixed architectures, limiting their adaptability to data variations. Our proposed algorithm leverages a self-adaptive strategy to dynamically adjust attention weights, fostering enhanced representation capabilities in GNNs. We introduce a novel attention weighting function that adapts to the data's inherent characteristics, improving the algorithm's robustness and effectiveness in various graph-structured data scenarios. The paper details the algorithm's implementation, provides a comprehensive analysis of its performance through benchmark datasets, and concludes with insights into the potential applications of this approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The goal of this paper is to develop a novel approach to automated generation using self-adaptive graph neural networks (SA-GNNs). Traditional graph neural networks (GNNs) often struggle with adapting to dynamic data, limiting their effectiveness in complex generative tasks. This work introduces a new model, termed SA-GNN, that dynamically adjusts the network's structure and parameters based on input data, enabling the generation of higher-quality outputs. We demonstrate the efficacy of this approach through a series of experiments utilizing various generative datasets, showcasing superior performance compared to existing state-of-the-art methods. This paper presents a complete framework for automated generation, focusing on a robust and adaptable approach to represent and generate data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel architecture for Graph Neural Networks (GNNs) designed to effectively handle multi-scale spatio-temporal data. The core concept revolves around decomposing time-series data into multiple scales, each represented by a corresponding graph structure. A GNN is then employed to learn dependencies within these individual scales, and finally, these graph representations are fused to achieve comprehensive modeling of complex spatio-temporal phenomena. The proposed approach addresses the limitations of traditional GNNs when dealing with large-scale, multi-scale data, offering improved capabilities for capturing intricate relationships. We demonstrate the effectiveness of this framework through a theoretical analysis, highlighting its advantages over existing methods. The key innovation lies in the hierarchical graph representation and the subsequent integrated learning process, leading to enhanced performance in capturing temporal dynamics and spatial correlations. This work provides a foundational framework for advanced spatio-temporal modeling applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embeddings are a fundamental tool for representing graph data, offering insights into network structure and relationships. Traditional methods often employ fixed weights, limiting the flexibility and scalability of the representation. This paper introduces a novel non-local graph embedding method that dynamically adjusts the weights of each node based on the global structure of the graph, promoting robust and scalable representation. We propose a neural network architecture that learns node weights based on the graph's connectivity and topology, enabling the method to capture complex relationships and adapt to varying graph characteristics. This approach overcomes the limitations of fixed weight methods, offering a significant advancement in graph embedding techniques. This work aims to provide a more adaptable and robust representation of graph data, facilitating a broader range of applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Neural Architecture Search (NAS) that leverages dynamic topology optimization based on dependency relationships. Traditional NAS methods often suffer from inefficiency and limited generalization due to the use of static search spaces and a lack of consideration for intricate layer-level dependencies within neural networks. Our method addresses these limitations by treating a neural network architecture as a graph, where nodes represent layers and edges represent dependencies. A dynamic search algorithm, guided by a dependency scoring function and topological constraints, iteratively modifies this graph's topology to explore a more efficient and effective search space. The core claim is that this dynamic approach, which explicitly models and optimizes topology, significantly improves both the efficiency and generalization capabilities of NAS. The algorithm incorporates a dependency scoring function that evaluates architecture changes based on dependency relationships and performance metrics, while topological constraints prevent drastic and potentially detrimental architectural alterations. The results demonstrate the effectiveness of this dynamic topology-dependent approach compared to conventional static NAS methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel neural network structure optimization method leveraging adaptive graph neural networks (GNNs). The core challenge in training GNNs lies in the dynamic adjustment of connection weights, which often requires extensive hyperparameter tuning. We introduce a new framework that dynamically adjusts the GNN's connectivity based on data-driven feature analysis. This adaptation mechanism, termed "Adaptive Weight Adjustment," significantly improves the network's expressiveness and generalization performance. We demonstrate the effectiveness of our approach through experiments on several benchmark datasets, showcasing improvements in both accuracy and convergence speed. The proposed method offers a promising alternative to traditional static network architectures and offers a robust solution for optimizing GNNs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
