108 papers indexed here
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This paper proposes a novel approach to knowledge discovery within graphs by introducing Self-Referential Graph Neural Networks (SR-GNNs). Traditional Graph Neural Networks (GNNs) operate passively, learning representations based solely on the graph structure and node features. This limits their ability to adapt and re...
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 dynamic topology optimization algorithm based on graph neural networks (GNNs). Traditional topology optimization methods often rely on manual design, struggling to adapt to complex systems exhibiting dynamic changes. Graph Neural Networks (GNNs) offer a powerful framework for automatically...
This paper proposes a novel network computation model termed "Space-Time Network Computation" (STNC). The core claim is that integrating spatial and temporal dimensions offers a more efficient approach to processing data exhibiting spatial and temporal dependencies. STNC leverages an extension of Graph Neural Networks...
This paper introduces a novel dynamic adaptive graph neural network (DAGNN) architecture designed to optimize network performance through automated structural adjustments. Traditional graph neural networks often rely on fixed, pre-defined structures, limiting their adaptability to diverse data. Our approach leverages a...
Predicting protein-protein interactions (PPIs) is a fundamental challenge in bioinformatics with significant implications for understanding biological systems and drug discovery. Traditional methods often struggle to capture the dynamic nature of PPIs, which are not static events but rather fluctuate over time due to v...
This paper presents a novel approach to complex system modeling leveraging the power of graph neural networks (GNNs). We construct a model designed to predict system behavior and states by effectively capturing relationships between nodes and edges within the system. The core mechanism of this approach is the utilizati...
This paper introduces a novel approach to neuro-symbolic reasoning utilizing cognitive graph structures. The core idea is to integrate the strengths of neural networks – their ability to learn complex patterns from data – with symbolic knowledge representations – providing explicit, interpretable reasoning. We propose...
This paper proposes a novel approach to analyzing time series data by combining Dynamic Time Warping (DTW) with Graph Neural Networks (GNNs). The core idea is to leverage DTW for effective feature extraction from time series, followed by a GNN to learn intricate relationships within the extracted representations. Tradi...
This paper proposes a novel approach to program optimization that leverages dynamic graph reasoning based on topology. Traditional static dependency graphs fail to adapt to runtime variations and resource constraints, leading to suboptimal performance. We introduce a system that utilizes a neural network to learn and r...
This paper investigates the representation of information flow within neural networks, proposing that the transmission of information is not a random process but rather governed by a specific topological structure. The core argument centers on the idea that the network's topology dictates the pathways through which inf...
This paper presents a novel approach to digital circuit synthesis leveraging Generative Graph Neural Networks (GNNs). The core claim is that designing complex digital circuits is a significantly time-consuming and iterative process. To address this, we propose a system that utilizes a GNN to automatically generate circ...
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