153 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper introduces a novel graph-based implicit modeling framework designed to generate and maintain complex, dynamic models for both biological systems and social systems. The core mechanism centers on the integration of graph neural networks and graph embedding techniques, enabling automated learning and represent...
Anomaly detection in complex networks – encompassing areas like fraud detection, intrusion detection, and equipment monitoring – presents a significant challenge. Traditional methods often employ simplistic thresholding techniques, which frequently lack robustness and adaptability to varying network characteristics. Th...
This paper proposes a novel approach to deep learning that integrates causal reasoning with Graph Neural Networks (GNNs). The core idea is to construct a neural network model capable of learning causal relationships and performing inference based on those relationships. Traditional deep learning models often struggle t...
Predicting protein-protein interactions (PPIs) is a critical task within structural genomics, aiming to understand cellular function and pathways. Traditional methods often rely on experimental techniques, which are costly and time-consuming. This research investigates the application of Graph Neural Networks (GNNs) to...
Predicting protein-protein interactions (PPIs) is a critical task within structural genomics, aiming to understand cellular function and pathways. Traditional methods often rely on experimental techniques, which are costly and time-consuming. This research investigates the application of Graph Neural Networks (GNNs) to...
This paper investigates the application of Relational Neural Networks (RNNs) to structured data exhibiting temporal dependencies. Traditional RNNs struggle to effectively represent complex relationships inherent in data where nodes are connected not just by proximity but also by defined interactions. We propose a novel...
This paper investigates the extraction of implicit semantic associations from multimodal data. The core claim is that humans inherently connect concepts across different modalities (e.g., images, text, audio) in subtle ways. We propose a novel framework leveraging deep learning to learn modality-specific representation...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including node classification, link prediction, and graph classification. However, existing GNN models often struggle to effectively capture the intricate relationships within graphs, particularly those with varying degrees of granula...
This paper presents a novel algorithm for automatically adjusting the parameters of graph neural networks (GNNs) using a self-adaptive approach. Traditional GNN parameter adjustment methods often rely on manual tuning, lacking the adaptability needed for complex datasets. This work introduces a new algorithm that lever...
This paper investigates a novel dynamic approach to adaptive neural network weight adjustment, specifically focusing on the utilization of graph neural networks (GNNs) for dynamic node weighting. We introduce a method that intelligently adjusts node weights during training based on the network's internal state and data...
This paper introduces a novel approach to multi-modal reasoning by integrating cognitive principles into the Graph Transformer architecture. We propose a 'cognitive memory' module that enhances the model's ability to capture and leverage past interactions and contextual information. The attention mechanism is then bias...
This paper introduces a novel approach to multi-modal reasoning by integrating cognitive principles into the Graph Transformer architecture. We propose a 'cognitive memory' module that enhances the model's ability to capture and leverage past interactions and contextual information. The attention mechanism is then bias...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
