108 papers indexed here
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This paper presents a novel approach to complex system modeling and simulation utilizing graph neural networks (GNNs). The core focus is to develop a method capable of automatically learning complex relationships within a system, and generating high-fidelity predictions. Traditional methods often struggle to effectivel...
This paper introduces a novel self-adaptive graph neural network (GNN) architecture based on dynamic graph representations. Traditional GNNs often struggle with the inherent limitations of static graphs, hindering their ability to effectively handle complex and dynamic data. Our proposed method leverages a dynamic grap...
Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future gra...
This paper introduces a novel method for complex system modeling based on graph neural networks (GNNs) and adaptive learning. Traditional approaches often require manual design of complex network structures, limiting the model's flexibility. Our method leverages GNNs to automatically learn and update graph structures,...
This paper addresses the limitations of current deep learning models in performing abstract symbolic reasoning. The core argument is that deep learning's reliance on pattern recognition hinders its ability to grasp underlying concepts and relationships, leading to a lack of genuine understanding. We propose a novel hie...
This paper introduces a novel neuro-symbolic reasoning framework centered around the dynamic construction of a knowledge graph. The core challenge in integrating neural networks and symbolic reasoning lies in the inherent differences in their representations and the difficulty in seamless translation between them. Our...
This paper proposes a novel graph neural network (GNN) optimization algorithm based on a dynamically adjusted attention mechanism. Traditional GNN optimization methods often rely on fixed architectures, limiting their adaptability to data variations. Our proposed algorithm leverages a self-adaptive strategy to dynamica...
The goal of this paper is to develop a novel approach to automated generation using self-adaptive graph neural networks (SA-GNNs). Traditional graph neural networks (GNNs) often struggle with adapting to dynamic data, limiting their effectiveness in complex generative tasks. This work introduces a new model, termed SA-...
This paper introduces a novel architecture for Graph Neural Networks (GNNs) designed to effectively handle multi-scale spatio-temporal data. The core concept revolves around decomposing time-series data into multiple scales, each represented by a corresponding graph structure. A GNN is then employed to learn dependenci...
Graph embeddings are a fundamental tool for representing graph data, offering insights into network structure and relationships. Traditional methods often employ fixed weights, limiting the flexibility and scalability of the representation. This paper introduces a novel non-local graph embedding method that dynamically...
This paper introduces a novel approach to Neural Architecture Search (NAS) that leverages dynamic topology optimization based on dependency relationships. Traditional NAS methods often suffer from inefficiency and limited generalization due to the use of static search spaces and a lack of consideration for intricate la...
This paper presents a novel neural network structure optimization method leveraging adaptive graph neural networks (GNNs). The core challenge in training GNNs lies in the dynamic adjustment of connection weights, which often requires extensive hyperparameter tuning. We introduce a new framework that dynamically adjusts...
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