This paper introduces a novel neural network architecture, termed "Adaptive Graph Neural Network" (AGN), that leverages dynamic graph structure learning to enhance data modeling and information extraction. Traditional neural networks typically employ static graph structures, limiting their ability to effectively capture complex relationships within data. AGN dynamically adjusts the graph structure during training, adapting to the specific characteristics of the data. This adaptive approach, combined with a novel graph-based representation learning technique, results in improved performance across various data analysis tasks. We present experimental results demonstrating the superior performance of AGN compared to conventional models. The core mechanism focuses on iterative graph refinement guided by a learned loss function that prioritizes information extraction from the graph. This paper explores the potential of dynamically structured neural networks to unlock new capabilities in data analysis and modeling.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to constructing knowledge graphs from implicit, multi-modal data. Traditional knowledge graph construction heavily relies on manual annotation, a process that is both time-consuming and expensive. Our method leverages the rich information contained within user behavior data – encompassing text, images, and videos – to automatically infer hidden knowledge and relationships. We employ deep learning models to represent these diverse modalities within a shared embedding space. Subsequently, we utilize similarity measures or graph neural networks to identify correlations and construct the knowledge graph. This approach significantly reduces the reliance on labeled data and offers a scalable solution for knowledge graph creation, particularly in scenarios where explicit knowledge bases are unavailable or incomplete. The core claim is that analyzing multi-modal user behavior data can effectively build a knowledge graph representing implicit knowledge and relationships. The key mechanism involves mapping different data modalities into a shared vector space using deep learning, followed by relationship discovery through similarity or graph neural networks. This work contributes to a more efficient and automated approach to knowledge graph construction, paving the way for intelligent applications that can reason and understand complex information.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing Relational Neural Networks (R-Nets) demonstrate effectiveness in various graph-based learning tasks; however, their performance often degrades significantly when applied to very large and complex graphs. This paper introduces a novel approach, Hierarchical Graph Decomposition (HGD), integrated within R-Net architectures, to address this scalability limitation. HGD employs a recursive decomposition strategy, breaking down the original graph into progressively smaller subgraphs. This hierarchical structure enables the network to operate on manageable components, improving computational efficiency and maintaining learning performance on large graphs. The core claim is that R-Nets can struggle with very large and complex graphs. The mechanism is the implementation of HGD, which recursively decomposes the graph. This addresses scalability issues of R-Nets via hierarchical graph decomposition.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Graph Neural Networks (GNNs) termed Contextual Graph Neural Networks with Memory Resonance (CGNMR). The core idea is that GNNs can significantly benefit from incorporating a mechanism that allows nodes to 'remember' and react to previously observed patterns, effectively propagating contextual information across the graph. The proposed CGNMR architecture integrates a recurrent connection within the GNN, creating a dynamic, context-aware representation. This allows nodes to not only process information from their immediate neighbors but also to leverage historical information, leading to improved performance in tasks such as node classification, link prediction, and graph classification. The key contribution lies in the synergistic combination of graph convolutional operations with recurrent memory networks, resulting in a more holistic understanding of graph structures and enhanced predictive capabilities. Mathematical formulations and notation are presented to explicitly define the model and its components.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the integration of high-dimensional data structures and graph neural networks to enhance data analysis and modeling capabilities. The core challenge lies in leveraging the strengths of both paradigms – the inherent connectivity of graphs and the ability of neural networks to learn complex patterns – to achieve significant improvements in efficiency and the discovery of underlying structural and relational insights within high-dimensional datasets. We present a novel framework that combines these elements, utilizing graph neural networks to accelerate analysis and modeling and automatically identify patterns. The paper details the design of the integration strategy, explores the benefits of this fusion approach, and discusses potential applications across various domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to knowledge graph reasoning utilizing dynamic graph structures. Traditional knowledge graph representation often struggles with the inherent uncertainty and evolving nature of real-world knowledge. We propose a framework that represents knowledge graphs as dynamic graphs, leveraging the power of Graph Neural Networks (GNNs) to perform reasoning tasks. This method allows for the effective capture of relationships and the ability to adapt to changes in the graph structure. The core of our approach lies in modeling entities and relationships as nodes and edges within a dynamic graph, and then employing GNNs to learn the underlying structural patterns and relationships for improved inference. We demonstrate the potential of this dynamic graph representation to enhance both the efficiency and accuracy of knowledge reasoning, addressing key limitations of static knowledge graph approaches. The results indicate a significant improvement in reasoning performance, particularly when dealing with incomplete or evolving knowledge.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to constructing knowledge graphs from implicit, multi-modal data. Traditional knowledge graph construction heavily relies on manual annotation, a process that is both time-consuming and expensive. Our method leverages the rich information contained within user behavior data – encompassing text, images, and videos – to automatically infer hidden knowledge and relationships. We employ deep learning models to represent these diverse modalities within a shared embedding space. Subsequently, we utilize similarity measures or graph neural networks to identify correlations and construct the knowledge graph. This approach significantly reduces the reliance on labeled data and offers a scalable solution for knowledge graph creation, particularly in scenarios where explicit knowledge bases are unavailable or incomplete. The core claim is that analyzing multi-modal user behavior data can effectively build a knowledge graph representing implicit knowledge and relationships. The key mechanism involves mapping different data modalities into a shared vector space using deep learning, followed by relationship discovery through similarity or graph neural networks. This work contributes to a more efficient and automated approach to knowledge graph construction, paving the way for intelligent applications that can reason and understand complex information.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to understanding and predicting complex system dynamics by leveraging the principles of self-organization and multi-scale event propagation. The core idea is to construct a self-organized event graph that mimics the behavior of biological neural networks, specifically focusing on the propagation of events across multiple scales. This is achieved through the design of a hierarchical network structure where different layers process event information at varying scales. An adaptive algorithm then dynamically adjusts node connections and event propagation pathways within the graph based on the observed event dynamics. The resulting event graph provides a powerful tool for capturing the intricate patterns and dependencies inherent in complex systems. Mathematical formulation focuses on event propagation, graph structure evolution, and the adaptive learning process. Key elements include event representation, graph adjacency matrix, propagation rules, and the objective function guiding network adaptation. The system is designed to learn patterns of complex behavior from data, offering a new perspective on modeling dynamic systems. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to compiler optimization decision modeling by leveraging Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on manually crafted rules and heuristics, often proving inadequate for complex codebases and emerging hardware architectures. This research proposes a probabilistic model built upon GNNs to represent and predict optimal optimization decisions. The model utilizes graph structures to encapsulate the intricate relationships within the compiler optimization process, with nodes representing optimization opportunities and edges signifying dependencies between them. Through neural network learning, the GNN dynamically adapts to the specific characteristics of the code, leading to more intelligent and effective optimization strategies. This work demonstrates the potential of GNNs to overcome the limitations of traditional methods and pave the way for automated, data-driven compiler optimization. The core claim is to utilize GNNs to build a probabilistic model for compiler optimization decisions, achieving intelligent optimization. The core mechanism involves modeling the compiler optimization process as a GNN, using graph nodes and edges to represent optimization factors and enabling learning and prediction through neural networks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) to the fields of program synthesis and optimization. The core idea is to represent program code as graph structures and leverage the power of GNNs to automatically learn the underlying structure and semantics. This approach offers a novel pathway for program development, moving beyond traditional, rule-based methods. We detail a framework where GNNs are trained on codebases, learning representations that can then be utilized for automated code generation or optimization tasks. The presented method aims to capture complex relationships within code, leading to more efficient and potentially novel solutions. The research contributes to a shift towards data-driven program development, utilizing the expressive power of GNNs to tackle previously challenging problems. The key contributions include the formalization of the process and a theoretical understanding of how GNNs can be effectively applied to this domain.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that addresses the limitations of existing methods in capturing the dynamic nature of evolving graphs. Traditional graph embedding techniques often rely on static representations, failing to account for the temporal changes in relationships between nodes. Our proposed method, Temporal Relational Graph Embedding with Dynamic Context (TRGDC), leverages a recurrent neural network to model the dynamic context of a graph, incorporating both relational information and the elapsed time since the last update. The core idea is to continuously adapt node embeddings based on these temporal and relational factors. We demonstrate that TRGDC significantly improves the representation of dynamic graphs compared to static embedding methods, achieving superior performance in downstream graph-related tasks. The key contributions of this work are the integration of temporal modeling with relational graph embedding and the utilization of a recurrent neural network for dynamic context representation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to program optimization leveraging the power of Graph Neural Networks (GNNs). The core idea is to represent program abstractions, such as Control Flow Graphs (CFGs) and Data Dependency Graphs (DDGs), as graphs and utilize GNNs to automatically identify optimization opportunities and generate optimized code. Traditional program optimization techniques often rely on hand-crafted heuristics and domain-specific knowledge. This approach offers a more automated and potentially more effective method by allowing the GNN to learn complex optimization patterns directly from the program's structure. We demonstrate the feasibility and potential of this approach through theoretical analysis and a conceptual framework. The key contribution lies in the application of GNNs to program optimization, providing a fundamentally new paradigm for achieving better code optimization. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
