70 papers indexed here
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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 captur...
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 – encomp...
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 arc...
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 propaga...
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 –...
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, leveragin...
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 – encomp...
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 th...
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 resea...
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 p...
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 pr...
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 a...
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