139 papers indexed here
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This paper introduces a novel approach to graph embedding that explicitly incorporates temporal dynamics and relational causality. Existing graph embedding methods often treat graphs as static structures, neglecting the evolving nature of relationships and the underlying causal mechanisms that govern them. We propose a...
This paper introduces a novel self-adaptive grammar rule engine based on graph neural networks. The engine leverages a graph representation of the input text to automatically infer grammatical rules and generate more accurate translations. We propose a method that dynamically adjusts the graph structure based on contex...
This paper investigates the application of neuromorphic computing architectures for real-time graph processing. Traditional graph processing relies on von Neumann architectures, which suffer from inherent bottlenecks due to the separation of processing and memory. We propose a novel approach utilizing spiking neural ne...
Existing graph embedding techniques predominantly focus on static graph structures, neglecting the crucial aspect of temporal dynamics inherent in many real-world networks. This paper introduces a novel approach – Temporal Graph Embedding with Relational Dynamics – that addresses this limitation. Our method leverages a...
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...
This paper introduces a novel approach to modeling social network dynamics using Hypergraph Neural Networks (HNNs). Traditional Graph Neural Networks (GNNs) struggle to accurately represent and propagate information through networks exhibiting higher-order relationships, a common characteristic of social structures. HN...
This paper investigates the potential of topological association mapping (TAM) to accurately decode and reconstruct complex neuronal memories. The core claim is that analyzing the dynamic topological association network between neurons provides a more precise method than traditional approaches. We propose a framework u...
This paper introduces a novel approach to graph embedding that combines the strengths of graph neural networks (GNNs) with symbolic representation learning. The core idea is to develop a system capable of learning both the structural and relational aspects of a graph through a dual mechanism. The system utilizes a GNN...
This paper introduces an Adaptive Graph Neural Network (AGNN) designed to leverage geometric information for enhanced feature extraction and learning. Traditional graph neural networks (GNNs) often rely solely on data, limiting their ability to exploit the inherent structure of the data. This research proposes a novel...
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 gr...
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 intro...
This paper introduces a novel approach to multimodal data fusion utilizing graph-to-graph models. The core idea is to represent diverse data modalities—including text, images, and audio—as interconnected nodes within a graph structure. This graph facilitates the fusion of information and enables reasoning across modali...
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