127 papers indexed here
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This paper presents a novel approach to graph neural networks (GNNs) based on adaptive graph structures. Traditional GNNs often employ static graph structures, limiting their ability to effectively capture complex relationships within the data. This research introduces a dynamic graph structure adjustment mechanism, dy...
This paper introduces a novel approach to modeling complex systems utilizing multi-scale graph neural networks (MS-GNNs). Complex systems, characterized by interconnected components operating at varying scales, pose significant challenges for traditional modeling techniques. This research proposes a framework that deco...
This paper proposes a novel approach to neural network architecture generation, termed "Neuron Circuitry Driven Dynamic Program Generation." The core claim is that comprehensive analysis of large-scale neuron circuits can automatically generate highly optimized neural network architectures and connection weights tailor...
Graph representation learning has emerged as a crucial technique in various domains, including social network analysis, drug discovery, and recommendation systems. However, many existing methods struggle to effectively capture long-range dependencies within graphs, leading to suboptimal representations. This paper intr...
This paper proposes a novel approach to representation learning termed "Emergent Topology Learning." The core idea is that neural networks, when appropriately designed, can autonomously discover and represent the underlying topology of complex data distributions. Traditional representation learning often focuses on lea...
Existing graph embedding techniques primarily focus on static graph representations, often neglecting the crucial aspect of temporal dynamics inherent in many real-world graphs. This paper introduces a novel approach combining relational graph embeddings with Temporal Graph Neural Networks (TGNNs) to address this limit...
Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal structural models. This work explores the application of Graph Neural Networks (GNNs) to address this cha...
This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks...
This paper introduces a novel approach to modeling complex systems utilizing multi-scale graph neural networks (MS-GNNs). Complex systems, characterized by interconnected components operating at varying scales, pose significant challenges for traditional modeling techniques. This research proposes a framework that deco...
Traditional methods for causal discovery often face challenges when dealing with intricate, high-dimensional datasets, frequently failing to accurately capture complex causal relationships. Furthermore, these methods typically lack the capability to simulate the consequences of interventions, hindering the ability to v...
The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis...
This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A gra...
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