This paper introduces a novel graph-based optimization algorithm specifically designed to address non-linear geometric constraints. Traditional optimization methods often struggle with complex geometric designs due to the inherent limitations of their geometric representation. This work proposes a framework leveraging graph neural networks (GNNs) to translate optimization problems into graph structures, enabling efficient and robust solution of these constraints. The algorithm's core mechanism focuses on dynamically learning optimal paths through the graph, effectively navigating and refining the geometric structure to achieve the desired outcome. We demonstrate the effectiveness of this approach through a series of illustrative examples, showcasing its ability to handle intricate geometric shapes and structural designs with significantly improved performance compared to conventional optimization techniques.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Autonomous robot navigation necessitates robust perception and decision-making capabilities that can adapt to dynamic and unpredictable environments. Traditional graph neural networks (GNNs) often rely on static graph structures, failing to effectively represent and reason about the constantly evolving relationships within a robot's surroundings. This paper proposes a novel framework utilizing Dynamic Graph Neural Networks (DGNNs) to address this limitation. The DGNN architecture incorporates dynamically adjusted node and edge attributes, driven by sensor data and the robot's internal state. This allows for real-time adaptation of the graph representation, improving the robot's ability to accurately perceive its environment and make informed navigation decisions. We demonstrate the potential of this approach through a theoretical analysis and a detailed specification of the DGNN design, emphasizing its advantages over static GNNs. The core claim is that static GNNs cannot adapt to the constantly changing environment experienced by autonomous robots. The key mechanism is the utilization of a GNN architecture with dynamically adjusted node and edge attributes based on sensor data and the robot's internal state, allowing for real-time adaptation of the graph representation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel dynamic graph neural network (GNN) architecture leveraging adaptive deep learning techniques to dynamically adjust graph connectivity based on input data. Traditional GNN models often require manual parameter tuning, limiting their adaptability to dynamic datasets. Our proposed approach employs a self-adaptive learning mechanism that continuously updates the graph's connectivity weights, ensuring optimal representation of complex relationships. This innovation addresses the limitations of static GNNs, offering a more robust and flexible solution for a wide range of applications. The paper details the architecture, training methodology, and experimental results demonstrating the effectiveness of this approach in various scenarios.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) for automated drug molecule design. Traditional drug discovery relies heavily on empirical methods and serendipitous findings, often suffering from high costs and low success rates. We propose a novel approach that leverages the inherent structural information within molecules by representing them as graphs and utilizing GNNs to learn the complex relationships between molecular structure and biological activity. The core claim of this work is the ability to automatically design drug molecules using GNNs. The mechanism involves graph representation learning and generation, enabling the model to predict and generate novel molecules with desired properties. This research offers a new paradigm for drug design, potentially accelerating the discovery process and improving the efficiency of identifying promising drug candidates. The key contributions of this work are the demonstrated effectiveness of GNNs in capturing structural features and predicting biological activity, and the framework for automated molecule generation. Further research will focus on incorporating more sophisticated molecular representations and expanding the scope of target diseases. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems, by effectively learning patterns from graph-structured data. However, a significant limitation of GNNs is their inherent opaqueness—the "black box" nature of their decision-making process. This paper addresses this challenge by proposing a novel approach integrating an explainability layer directly into a GNN architecture. This layer aims to provide transparent and interpretable explanations for the network's predictions, focusing on identifying the key structural features within the graph that contribute to the outcome. Specifically, we introduce a mechanism to highlight influential nodes and edges, offering insights into *why* a GNN made a particular prediction. This work enhances the trustworthiness and usability of GNNs by promoting a deeper understanding of their internal workings. The core contribution lies in bridging the gap between predictive performance and interpretability in graph learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Graph Neural Networks (GNNs) for the automated identification and analysis of complex network topologies. Traditional network analysis methods often struggle to capture the intricate relationships and emergent patterns found in complex networks, such as social networks, biological networks, and technological networks. GNNs offer a powerful approach by directly leveraging the graph structure itself as the primary data source. This work proposes a framework utilizing Graph Convolutional Networks (GCNs) and explores their effectiveness in learning topological features and predicting network evolution. The core claim is that GNNs provide a novel methodology for complex network analysis, enabling automated identification and predictive modeling. This research contributes to a deeper understanding of complex network behavior and opens possibilities for applications in various domains. The methodology focuses on feature extraction using GCNs and ultimately predicts future network states based on learned representations. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) for causal discovery directly from observational data. Traditional causal inference methods often rely on strong assumptions regarding underlying causal mechanisms, which can be difficult to validate. We propose a novel framework where a GNN learns causal relationships by analyzing the correlations within the data. The core idea is to train the GNN on observational data to generate node embeddings that encode both correlations and causal dependencies. We utilize techniques like mutual information maximization and intervention prediction to further refine the learned graph structure, enhancing the accuracy of the discovered causal relationships. This data-driven approach offers a flexible and potentially more robust method for causal discovery compared to traditional approaches, particularly when limited prior knowledge about the system is available. The methodology presented here leverages the representational power of GNNs to effectively extract causal signals from complex observational datasets.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to modeling human memory by representing it as a dynamic cognitive graph. The core idea is to map the complex neural activation patterns observed during memory processes onto a graph structure where nodes represent concepts and edges represent the strength and temporal relationships between them. We leverage neuroimaging data, specifically fMRI, to construct this cognitive graph, effectively translating brain activity into a network representation. The simulation of memory retrieval then involves traversing this graph to reconstruct the original memory state. This biologically-inspired method offers a new perspective on the neural basis of memory retrieval, potentially explaining how the brain efficiently encodes, stores, and retrieves information. The framework presented here aims to provide a more nuanced and dynamic understanding of memory compared to traditional static models. Key elements include the definition of graph nodes and edges, the process of graph construction from neuroimaging data, and the algorithmic framework for simulating memory retrieval. Further research will focus on refining the graph construction process and exploring the impact of different graph parameters on memory retrieval performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Adaptive Self-Design Algorithm is a novel algorithmic framework designed to automate the design and optimization of mathematical formulas, particularly within complex non-standard and nonlinear equation systems. This approach leverages a probabilistic search algorithm, incorporating reinforcement learning and genetic algorithms, to iteratively refine formula parameters and capture the intricate structure of the formula. The algorithm's key innovation lies in the development of a "probability search" mechanism, coupled with graph neural networks to enhance formula complexity understanding. This represents a significant advancement over existing automated formula design methods, which often rely on predefined rules and limited flexibility. The proposed algorithm demonstrates the potential to overcome human limitations in formula creation, offering a robust and adaptable solution for a diverse range of mathematical challenges.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to symbolic artificial intelligence (AI) that addresses the limitations of traditional, manually-engineered rule-based systems. The core challenge in symbolic AI has historically been the extensive and often laborious process of crafting and maintaining a comprehensive rule set. Existing systems frequently exhibit brittleness and a lack of adaptability to changing environments. To mitigate these issues, we propose a system architecture leveraging reinforcement learning (RL) to dynamically generate and refine a symbolic rule base. The system operates by employing an RL agent that observes the system's performance and iteratively adjusts the rules to optimize its behavior. This dynamic approach significantly reduces the manual effort required for rule engineering while enhancing the system's robustness and adaptability. We detail the key components of this system, including the rule representation, the RL agent's reward function, and the rule modification mechanisms. The system's core claim is that a dynamically generated and refined rule base offers a more robust and adaptable solution compared to static, manually-engineered rule sets. The system's ability to learn and adapt to data-driven feedback represents a significant advancement in the field of symbolic AI. The presented method demonstrates a pathway toward creating AI systems that can autonomously evolve their knowledge representation, ultimately leading to greater flexibility and efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Policy Optimization (PPO), we can significantly improve network efficiency and generalization capabilities. NTAD employs a "Neuro-Topology Evaluator" – a neural network itself – to continuously assess the performance of the current connection graph, considering metrics such as accuracy, latency, and energy consumption. The PPO algorithm then adjusts connection weights and introduces or removes neurons to maximize the evaluator's score. Unlike traditional training methods relying on static graphs and fixed optimization targets, NTAD offers a dynamic, self-optimizing solution capable of handling non-static, high-dimensional input data and autonomously discovering optimal neural connection patterns. The methodology presented here represents a significant advancement in neural network design, shifting the focus from solely adjusting weights to actively shaping the network's physical structure.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Adaptive Spatiotemporal Neural Models (ASPNM), a novel approach to time series prediction that addresses the limitations of traditional, static neural network models. The core idea is to design a neural network architecture capable of dynamically adjusting its internal parameters and behavioral patterns based on the characteristics of the input time series and its historical information. This adaptation is achieved through a combined mechanism utilizing Recurrent Neural Networks (RNNs) for state representation, reinforcement learning for parameter optimization, and genetic algorithms for behavioral pattern refinement. The resulting ASPNM models demonstrate improved prediction accuracy compared to conventional models, particularly when dealing with complex and non-stationary time series data. The key innovation lies in the model's ability to learn and adapt, mimicking the dynamic nature of real-world time series phenomena. The models are evaluated using various benchmark datasets and demonstrate superior performance across diverse scenarios. This work presents a promising direction for enhancing time series prediction capabilities.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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