This paper proposes a novel approach to analyzing neural activity termed "Neural Time Flow Mapping." The core idea is to leverage the temporal information inherent in neuronal activity patterns to establish dynamic time flow maps between neurons. This allows for predictive modeling and a deeper understanding of neural activity. The method utilizes recurrent neural networks (RNNs) to learn temporal sequences from neuronal data, constructing a neural time flow graph. Graph embedding techniques are then applied to generate neural state representations, ultimately enabling prediction and pattern recognition. The novelty lies in treating neural activity as a dynamic process, shifting the focus from static neuron states to their temporal evolution, offering a new perspective for neuroscience research. The system is designed to capture complex, non-linear relationships within neuronal networks, providing a more nuanced understanding of brain function. Key performance metrics include prediction accuracy and graph embedding quality, which are evaluated using standard metrics such as Mean Squared Error (MSE) and Normalized Cross-Entropy (NCE). The ultimate goal is to build a predictive model capable of accurately forecasting future neuronal activity based on observed temporal patterns.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Accurate urban traffic flow prediction is crucial for intelligent transportation systems, urban planning, and resource allocation. Traditional traffic forecasting methods often struggle to capture the complex spatiotemporal dependencies inherent in urban traffic networks. This paper proposes a novel approach utilizing multi-scale spatio-temporal graph neural networks (MSST-GNNs) to address this challenge. The core idea is to integrate traffic data at various scales – street, regional, and city levels – and leverage the power of graph neural networks to learn intricate traffic patterns. The MSST-GNNs construct a graph representation of the urban road network, where nodes represent road segments and edges represent connectivity. The model then employs a multi-scale architecture to capture both spatial and temporal dependencies effectively. Specifically, we propose a hierarchical graph convolutional network (HGCN) that progressively aggregates information from finer to coarser scales, incorporating both spatial and temporal context. The HGCN is integrated within a recurrent neural network (RNN) to model temporal dynamics. The resulting MSST-GNNs achieve state-of-the-art performance on several benchmark urban traffic flow datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving prediction accuracy compared to traditional methods and existing graph neural network models. The key contributions of this work are the novel MSST-GNN architecture, the hierarchical graph convolutional network, and the effective integration of multi-scale spatio-temporal data.
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 knowledge graph reasoning. However, standard GNNs often struggle to effectively capture intricate relational dependencies between nodes. This work introduces a novel approach that integrates attention mechanisms into the message passing process within GNNs, enabling nodes to selectively focus on their most relevant neighbors. The core idea is to allow the network to learn which relationships are most important for a given node's representation, thereby improving its ability to reason about complex relational patterns. We detail the architecture and training procedure, demonstrating enhanced performance on benchmark relational reasoning tasks. The key innovation lies in the dynamic weighting of neighbor contributions during message aggregation, leading to more robust and accurate node embeddings. The resulting model achieves superior performance compared to traditional GNNs, particularly when dealing with datasets exhibiting complex relational structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predicting protein function and understanding protein-protein interactions (PPIs) are central challenges in modern biology. Protein-protein interaction networks, representing these interactions, are inherently complex and dynamic. Traditional methods often struggle to capture the temporal evolution of these networks, failing to account for changes in interaction strengths and patterns over time. This paper proposes a novel framework for learning representations of PPI networks that integrates both structural and temporal information. We leverage graph neural networks (GNNs) to encode the network topology and incorporate temporal convolution layers to model the temporal dynamics of interactions. Furthermore, attention mechanisms are utilized to focus on the most relevant interactions within the network context. The resulting learned representations can then be used for various downstream tasks, such as predicting protein function, identifying novel PPIs, and forecasting network changes. Our approach demonstrates a significant improvement in prediction accuracy compared to existing methods, highlighting the importance of temporal dynamics in PPI network analysis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates a novel approach to solving non-linear equation systems using graph neural networks (GNNs). Traditional methods often struggle with complex systems, requiring significant computational resources. We propose a method that leverages GNNs to transform the equation into a graph representation, allowing for automated exploration and optimization of solutions. The core of our work lies in developing a GNN architecture specifically designed for this task, incorporating a graph structure that facilitates efficient search and convergence. We demonstrate the effectiveness of this approach through a series of illustrative examples, showcasing significant improvements in solution speed and accuracy compared to established methods. Furthermore, we explore the potential of GNNs to handle larger and more intricate equation systems, highlighting their applicability to a broader range of problems.
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 knowledge graph reasoning. However, standard GNNs often struggle to effectively capture intricate relational dependencies between nodes. This work introduces a novel approach that integrates attention mechanisms into the message passing process within GNNs, enabling nodes to selectively focus on their most relevant neighbors. The core idea is to allow the network to learn which relationships are most important for a given node's representation, thereby improving its ability to reason about complex relational patterns. We detail the architecture and training procedure, demonstrating enhanced performance on benchmark relational reasoning tasks. The key innovation lies in the dynamic weighting of neighbor contributions during message aggregation, leading to more robust and accurate node embeddings. The resulting model achieves superior performance compared to traditional GNNs, particularly when dealing with datasets exhibiting complex relational structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Epistemic Graph Neural Networks (EGNNs), a novel approach to graph neural networks that explicitly models and incorporates uncertainty into the learning process. Traditional GNNs primarily focus on learning pattern recognition from graph-structured data, often neglecting the inherent uncertainty associated with these patterns and the relationships between nodes. EGNNs address this limitation by representing the network's confidence in inferred relationships as dynamic edge weights within a graph. Nodes in the graph represent individual data points, and edges represent the strength of the inferred relationships between them. Crucially, the weight of each edge is modulated by a learned epistemic uncertainty estimate, reflecting the network's confidence in that particular connection. This allows EGNNs to not only learn patterns but also to represent and reason with uncertainty, leading to more robust and reliable predictions and decision-making. The core claim of this work is that neural networks can learn not just patterns in data, but also the *uncertainty* inherent in those patterns, represented as a dynamically evolving graph. The mechanism is a GNN where nodes represent data points and edges represent the strength of inferred relationships, with edge weights modulated by learned epistemic uncertainty estimates – representing the network's confidence in the relationship.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Contextualized Semantic Graph Networks (CSGN), a novel approach to knowledge representation and reasoning that addresses the limitations of traditional static knowledge graphs. The core idea is that semantic understanding fundamentally relies on a graph representation that dynamically adapts to the *context* of the information being processed. CSGN employs a graph neural network where nodes and edges are not static; rather, they evolve their attributes and relationships based on the surrounding context, thereby incorporating new information and adjusting semantic weights in real-time. This dynamic adaptation allows CSGN to handle complex, evolving data with a far greater degree of nuance and adaptability compared to static knowledge graphs. The framework is designed to improve performance in tasks requiring an understanding of interconnected information where context plays a crucial role. We demonstrate the potential of this approach through a theoretical framework and outline key design considerations.
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
Network intrusion detection systems (NIDS) face increasing challenges due to the dynamic and evolving nature of cyber threats. Traditional graph neural network (GNN) approaches often struggle to adapt effectively to these changes, relying on static graph structures and pre-defined node features. This paper proposes a novel self-organizing GNN architecture designed specifically for network intrusion detection. The core idea is to enable the GNN to continuously learn and adapt its node representations and connection topology based on real-time network traffic patterns and intrusion detection alerts. This dynamic adaptation allows the system to respond effectively to emerging threats and maintain high detection accuracy even in highly volatile network environments. The proposed architecture incorporates a feedback loop that utilizes detected intrusions to refine the graph structure and node embeddings, creating a resilient and adaptive NIDS. We demonstrate the potential of this approach through a theoretical framework and outline key design considerations, providing a foundation for future research and development in this critical area. The central contribution is a method for dynamically updating the GNN's representation of the network, addressing a major limitation of static GNN models in the context of cybersecurity.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Graph Neural Networks (GNNs) that addresses a critical limitation of traditional GNNs – their inability to effectively model the dynamic evolution of graph structures. We propose Dynamic Graph Neural Networks with Contextual Memory Encoding (DGMNE), a framework that integrates a contextual memory component to explicitly capture and adapt to the temporal changes within a graph. The core idea is to augment standard GNN layers with a separate memory module. This module stores embeddings representing past graph states, weighted according to their temporal proximity and relevance. The weighting scheme allows the network to learn and dynamically update its understanding of the graph's topology based on its historical evolution. This approach significantly improves the representation learning capabilities of GNNs, leading to enhanced performance in tasks where graph dynamics are paramount. We demonstrate the effectiveness of DGMNE through a theoretical analysis and outline the key components and operational principles. The proposed architecture offers a flexible and powerful method for incorporating temporal information into GNNs, opening up new avenues for research and applications.
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
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