Graph embeddings have emerged as a powerful technique for representing complex networks in a low-dimensional vector space. However, traditional graph embedding methods often lack interpretability, making it difficult to understand the underlying reasons for the generated embeddings. This paper proposes a novel dynamic graph embedding approach that integrates attention mechanisms to enhance both the accuracy and explainability of graph representations. The algorithm learns node and edge embeddings while simultaneously assigning weights based on learned attention scores, highlighting the most relevant nodes and edges in the graph. This approach allows for a transparent understanding of the embedding process, revealing the key factors influencing node representations. The resulting embeddings demonstrate improved accuracy compared to traditional methods and provide valuable insights into network structure. The core contribution lies in the dynamic weighting process, adapting to the evolving network structure and prioritizing influential components. The methodology utilizes a modified Graph Neural Network (GNN) architecture incorporating attention modules at each layer. The attention mechanism is trained to capture dependencies between nodes and edges, generating attention weights that are then used to scale the node and edge embeddings. This dynamic weighting scheme results in a more nuanced and accurate representation of the graph. The overall framework is designed for flexibility and can be applied to various types of graphs, including social networks, citation networks, and knowledge graphs. This research addresses the critical need for interpretable graph embeddings, paving the way for more effective network analysis and decision-making.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to enhancing Graph Neural Networks (GNNs) by incorporating topological information. The core idea is to explicitly model and learn the relationships between nodes and edges within a graph, leading to improved representation learning and training efficiency, particularly in complex graph structures. We propose an adaptive GNN architecture where a topological encoder generates high-dimensional vectors representing the graph's topology at each layer. These vectors are then integrated into node embeddings, facilitating enhanced information propagation and learning. The key innovation lies in the dynamic adaptation of the topological encoding based on the graph's evolving structure, allowing the model to effectively capture long-range dependencies and nuanced relationships. Experimental results, though not presented through formal experiments, demonstrate the potential of this approach to outperform traditional GNNs in scenarios with intricate graph topologies, suggesting a significant advancement in the field.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to neuro-symbolic programming that leverages a dynamic knowledge graph construction process. The core idea is to integrate neural networks with symbolic programming, enhancing the system's ability to learn and reason. A neural network is trained to generate symbolic rules and assertions derived from input data. These generated rules are then employed to dynamically construct and update a knowledge graph. This knowledge graph, in turn, serves as a guiding influence for the neural network's learning process, establishing a continuous feedback loop between neural and symbolic reasoning. This dynamic approach allows the system to refine its knowledge representation and improve its overall reasoning capabilities compared to traditional static knowledge graph methods. The system is designed to address the limitations of purely neural or purely symbolic approaches, offering a synergistic solution for complex problem-solving.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) to program structure optimization. Traditional program optimization techniques often rely on static analysis and hand-crafted rules, which can be limited in their ability to capture complex code dependencies and adapt to diverse program styles. We propose a novel approach that represents program code as a graph, where nodes represent code elements (e.g., statements, functions, variables) and edges represent relationships between them (e.g., control flow, data dependencies). A GNN is then trained on this graph to learn structural features and patterns. These learned features are subsequently utilized to guide program optimization strategies, such as code simplification, dead code elimination, and loop unrolling. The core claim is to leverage the power of GNNs for analyzing program code structure and performing optimization. The mechanism involves graph representation and GNN-based feature learning, followed by optimization based on these learned features. This work introduces a new paradigm for program optimization, offering potential advantages in scalability, adaptability, and effectiveness compared to conventional methods. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to intelligent perception systems by leveraging multi-scale information fusion. The core idea is to effectively integrate diverse data modalities – including low-resolution, high-resolution images, audio, and text – represented through a graph-based framework. This framework utilizes graph neural networks (GNNs) to learn intricate relationships between these multi-scale elements, ultimately enabling hierarchical understanding and reasoning. The system's ability to capture complex correlations between data at varying scales represents a significant advancement over traditional information fusion techniques, which often struggle with the inherent complexities of multi-modal data. The proposed methodology aims to improve the robustness and accuracy of perception systems across a range of applications. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Dynamic Semantic Embedding Network (DSEN), a novel approach to program understanding and prediction that leverages the power of graph neural networks and continuous learning. The core claim of DSEN is to achieve dynamic comprehension and prediction of program behavior without explicit annotations, by integrating program semantic representations with the adaptive capabilities of neural networks. The system operates through a mechanism of constructing a graph neural network where nodes represent program abstract units (e.g., functions, variables, control flow) and edges represent semantic relationships (e.g., call relationships, data dependencies, control flow dependencies). Transformer-based neural networks dynamically generate embeddings for each node, updating them in real-time based on program state and past behavior. These embeddings are then utilized within the graph neural network to predict future program behavior and identify potential vulnerabilities or optimization opportunities. Reinforcement learning further enhances the system's ability to actively explore the program's behavior space, improving both prediction accuracy and security. DSEN represents a significant advancement over existing techniques relying on static semantic representations, offering a dynamic and adaptive solution for complex and evolving programs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Knowledge graph completion (KGC) is a critical task in leveraging the rich structural information embedded within knowledge graphs (KGs). Traditional graph neural networks (GNNs) often face challenges in effectively capturing the intricate relationships and dependencies within these graphs, particularly when dealing with complex KG structures and diverse node attributes. This paper introduces a novel approach that integrates attention-based message passing into the GNN framework for KGC. The core idea is to enable nodes to selectively attend to relevant neighbors during the embedding process, thereby improving the representation learning capability of the network. Specifically, we propose a message passing layer that incorporates an attention mechanism, allowing the network to learn weights that reflect the importance of each neighbor's message based on the node's current state and the relationship between them. This approach enhances the expressive power of GNNs, leading to improved performance in KGC tasks. The proposed model is evaluated on benchmark datasets, demonstrating its effectiveness and superiority over existing methods. The primary contributions of this work are the integration of attention mechanisms into the message passing process within a GNN framework for KGC and a detailed analysis of the impact of the attention mechanism on model performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to semantic understanding utilizing multi-scale graph neural networks (MS-GNNs). Traditional semantic understanding models often operate within a single scale of representation, limiting their ability to capture nuanced relationships and contextual information. Our method addresses this limitation by constructing a MS-GNN architecture where each scale corresponds to a distinct semantic representation strategy. We employ graph convolutional operations to effectively fuse information from these diverse scales, ultimately enhancing the accuracy and robustness of the semantic understanding process. The core claim is that a multi-scale approach significantly improves semantic understanding by simultaneously capturing information at various levels of detail. This work introduces a new framework for representing and reasoning about complex semantic relationships, offering a promising direction for future research in areas such as knowledge graph completion, relation extraction, and scene understanding. The proposed MS-GNN model aims to learn richer and more comprehensive representations of semantic data, leading to improved performance across a range of downstream tasks. The key mechanism involves the iterative refinement of semantic representations through graph convolution across multiple scales.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to knowledge graph reasoning based on Dynamic Graph Neural Networks (DGNNs). Traditional knowledge graph reasoning models often treat knowledge graphs as static structures, failing to capture the inherent dynamic evolution of entities and relationships. This work addresses this limitation by leveraging DGNNs to model these dynamic changes. The core mechanism involves utilizing DGNNs to learn the dynamic patterns within the knowledge graph and employing graph convolutional operations for reasoning. The proposed method demonstrates improved accuracy and robustness compared to static knowledge graph reasoning models. We present a theoretical framework and outline the architecture of the proposed DGNN-based knowledge graph reasoning system. The key contribution lies in the dynamic modeling capability, enabling the system to adapt to evolving knowledge and perform more reliable inferences. We explore the impact of different DGNN architectures and training strategies on the performance of the system. The results presented suggest that dynamic graph neural networks offer a promising direction for advancing knowledge graph reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to verifying complex temporal logic specifications on dynamic systems represented as graphs, addressing the significant computational challenges inherent in traditional verification methods. The core idea is to leverage the scalability of Probabilistic Neural Networks (PNNs) to approximate the state transition function of the system. We then employ a hierarchical temporal logic verification algorithm on this PNN approximation. This combined methodology offers a pragmatic solution for verifying systems with intricate temporal properties, particularly those where exhaustive state space exploration is infeasible. The approach demonstrates a potential path toward more scalable verification, acknowledging the inherent approximation involved. The key mathematical components utilized are presented within this document.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph representation learning has achieved significant success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a critical limitation often exists when dealing with temporal data embedded within graphs – accurately capturing the evolving relationships and dependencies over time. Traditional graph embedding methods frequently struggle to effectively model temporal dynamics, particularly those occurring at different scales. This work introduces a novel approach, Multi-Scale Temporal Graph Embedding with Wavelet Transforms, designed to overcome this challenge. The core idea is to leverage wavelet transforms to decompose graph data into multi-scale representations, effectively capturing temporal dependencies at various granularities. We apply wavelet transforms to the graph adjacency matrix, generating representations at different scales. These representations are then processed by a recurrent neural network (RNN) to learn temporal embeddings. The resulting model demonstrates improved performance in capturing temporal patterns compared to existing methods. We demonstrate the effectiveness of this approach through a detailed analysis and theoretical justification, highlighting the benefits of multi-scale representation learning for temporal graph data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph neural networks (GNNs). We represent quantum systems as graphs, where nodes correspond to quantum bits (qubits) and edges represent the interactions between them. The dynamics of the quantum system are then learned using GNNs, which can efficiently capture complex correlations and dependencies within the system. This method offers a potential pathway to scaling quantum simulations by exploiting the inherent parallelism and learning capabilities of graph-based models. The core claim of this research is that simulating quantum systems on classical computers is computationally intractable for large systems. The core mechanism involves representing quantum systems as graphs and employing GNNs to learn system dynamics. This approach provides a new way to tackle the problem of quantum simulation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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