This paper introduces Temporal Topology, a novel system for predicting future topology based on event streams. We propose a Temporal Graph Network, a neural network trained to learn geometric patterns from sequences of events, allowing for dynamic analysis and predictive modeling. The system's core claim centers around leveraging real-time data to create a system that goes beyond static geometric analysis, incorporating a dynamic, predictive element. This approach addresses the limitations of traditional geometric modeling by incorporating temporal dynamics, offering a more responsive and insightful approach to topology analysis. We provide a detailed overview of the Temporal Graph Network architecture, training methodology, and preliminary results demonstrating its effectiveness in generating and analyzing geometric patterns.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as graph problems, where nodes represent system components and edges represent their relationships. GNNs are then employed to learn representations of these nodes based on their connections, enabling the prediction of potential security risks. This approach offers improved accuracy compared to conventional methods by incorporating contextual information and learning complex dependencies within the system. The core claim is that system security problems can be modeled as graph problems, utilizing GNNs to analyze system vulnerabilities. The key mechanism involves representing system components and relationships as a graph, and employing GNNs to learn node relationships and predict security risks. This work contributes to a more proactive and comprehensive understanding of system security.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, traditional graph embedding methods often treat nodes and edges independently, neglecting the crucial relational context that governs the structure of the graph. This paper introduces an adaptive graph embedding method designed to address this limitation. The core idea is to learn embeddings by explicitly incorporating the relationships between nodes, leveraging the contextual information surrounding each connection. We employ a recurrent neural network (RNN) to process this relational context, generating embeddings that are sensitive to these contextual dependencies. The resulting embeddings are expected to provide more accurate and informative representations of nodes compared to methods that disregard relational context. This work demonstrates the potential of incorporating contextual information into graph embedding, leading to improved performance across a range of graph-based tasks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to cognitive computing centered around the symbolic simulation of human reasoning. The core idea is to build cognitive systems by explicitly modeling the underlying processes of human thought, leveraging constraints, contextual knowledge, and probabilistic inference. We introduce a hybrid system combining neural networks for perceptual tasks with a symbolic reasoning engine. This engine utilizes a knowledge graph to represent domain knowledge and employs probabilistic inference techniques to emulate human-like reasoning. The approach offers a more granular simulation compared to existing methods, focusing on the detailed mechanisms driving human cognition rather than solely relying on high-level behavior imitation. The system architecture is detailed, including the integration of perception and reasoning modules, and the methodology for knowledge graph construction and probabilistic inference is outlined. The significance of this work lies in its potential to create genuinely intelligent systems capable of robust reasoning and adaptation within complex, real-world environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel dynamic graph neural network (D-GNN) architecture, termed "Adaptive Dynamic Graph Neural Network," designed to optimize the learning process of GNNs through dynamic adjustment of the graph's structure and parameters. Traditional GNN approaches often rely on fixed graph structures, limiting their adaptability to diverse datasets. This work proposes a mechanism that continuously refines the graph structure during training, enabling the network to better capture complex relationships within the data. We demonstrate the effectiveness of our approach through extensive experiments on benchmark datasets, showcasing significant improvements in both accuracy and convergence speed compared to existing GNN methods. The core of our design lies in a novel dynamic adjustment strategy that leverages a reinforcement learning component to guide the graph's evolution. The resulting D-GNN exhibits improved generalization performance and robustness to variations in input data. Finally, we discuss potential future research directions and limitations of this approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel framework for constructing and reasoning with dynamic semantic graph knowledge. The core idea is to build a graph that not only represents entities and their relationships but also incorporates dynamic semantic states (events, emotions, temporal changes) and their interactions. This dynamic graph is continuously updated using deep learning techniques, enabling probabilistic inference based on the graph structure and state. The system leverages techniques such as Variational Autoencoders (VAEs) and Graph Neural Networks (GNNs) to learn complex patterns within the graph. Furthermore, it integrates time series analysis and sentiment analysis to model temporal and emotional dynamics. The key contribution lies in the ability to represent and reason with evolving semantic knowledge, moving beyond the limitations of traditional static semantic graphs. The system aims to provide a robust foundation for applications requiring real-time understanding and inference, such as intelligent agent systems and dynamic knowledge management.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research introduces an empathetic dimension into the modeling process, leveraging machine learning algorithms to learn and represent emotional relationships and behavioral patterns within networks. The core claim is to build a framework capable of simulating human interactions by incorporating emotional factors, thereby facilitating a deeper understanding of complex phenomena observed in social networks. The methodology involves constructing a network where nodes represent individuals and edges represent connections, but crucially, these edges are augmented with associated emotional states and behavioral probabilities. We utilize machine learning techniques – specifically, recurrent neural networks (RNNs) and graph neural networks (GNNs) – to learn the dynamics of these emotional and behavioral relationships. The resulting model can then be used to simulate network behavior, predict individual actions, and explore emergent social phenomena. This approach offers a significant advancement over existing network models by providing a more nuanced and realistic representation of human social dynamics. The research aims to provide a foundation for understanding and predicting social behavior, with potential applications in areas such as social influence, crisis management, and community resilience.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel probabilistic inference network designed to leverage the correlations inherent within multi-modal data. The core idea is to construct a network architecture that explicitly models and utilizes relationships between different data modalities. This is achieved through the application of Graph Neural Networks (GNNs), which are employed to process the multi-modal data represented as a graph. Nodes in the graph represent individual data instances or features, and edges represent the learned associations between them. This approach allows the network to perform probabilistic inference by propagating information across related nodes, ultimately leading to more accurate and reliable predictions. The key innovation lies in the explicit incorporation of multi-modal associations into the GNN framework, moving beyond traditional GNN applications focused solely on single-modality data. We demonstrate the effectiveness of this approach through theoretical analysis and outline its potential for applications in domains requiring sophisticated probabilistic reasoning from diverse data sources.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs). This framework addresses the limitations of traditional federated learning, particularly when dealing with graph-structured data, where raw data sharing poses significant privacy concerns. The system avoids direct data exchange by allowing devices to collaboratively learn through the exchange of model updates, preserving data privacy while enabling effective knowledge transfer. The proposed FedGNN architecture consists of local graphs and locally trained GNNs, synchronized via a novel aggregation mechanism. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and outline the key components required for implementation. The system is designed to be scalable and adaptable to various decentralized network topologies. This work represents a significant step towards privacy-preserving collaborative learning in complex, graph-based environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Neuro-Symbolic Reasoning via Dynamic Graph Construction (NSRC-DG) presents a novel approach to reasoning that integrates the strengths of neural networks and symbolic reasoning. Traditional reasoning systems often struggle with complex, nuanced inferences requiring deep understanding and logical deduction. This work introduces NSRC-DG, a system designed to dynamically construct and refine logical inferences based on observed data, leveraging neural networks to generate initial graph structures and then iteratively refining them through symbolic deduction. The core mechanism focuses on creating a layered graph structure where nodes represent concepts and edges represent relationships, allowing for efficient exploration and refinement of reasoning paths. This approach contrasts with reactive systems, offering a proactive and adaptable framework for enhanced reasoning capabilities. The system's success will be demonstrated through a series of benchmark problems designed to test its ability to generalize and reason effectively.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Temporal Topology Modeling for Signal Processing addresses the limitations of traditional signal processing methods by introducing a novel temporal topology model. This model leverages the combined power of recurrent neural networks and graph theory to effectively capture the evolution of signal patterns over time. The core mechanism involves establishing and maintaining a dynamic graph structure that represents the temporal dependencies within a signal, enabling more accurate and robust analysis. The proposed approach offers a significant advancement in signal processing, particularly for complex and long-term signal characteristics.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
