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Modified graph systems for distributed optimization
In distributed optimization theory, network topology graphs are important in communications among multiple agents. However, distributed optimization...
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Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder
Graph contrastive learning (GCL) has been successfully used to solve the problem of the huge cost of graph data annotation, such as labor cost, time...
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Large graph layout optimization based on vision and computational efficiency: a survey
Graph layout can help users explore graph data intuitively. However, when handling large graph data volumes, the high time complexity of the layout...
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A survey on uncertain graph and uncertain network optimization
Uncertainty theory, founded in 2007, has become a branch of mathematics to model uncertainty rather than randomness. As an indispensable part of...
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Graph neural networks for deep portfolio optimization
There is extensive literature dating back to the Markowitz model on portfolio optimization. Recently, with the introduction of deep models in...
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DC-Graph: a chunk optimization model based on document classification and graph learning
Existing machine reading comprehension methods use a fixed stride to chunk long texts, which leads to missing contextual information at the...
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Scalable decoupling graph neural network with feature-oriented optimization
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph neural networks (GNNs), being an...
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Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm
The combination of a database, graph neural network, and an optimization algorithm is an effective approach for crystal structure prediction (CSP)....
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Aerodynamic shape optimization using graph variational autoencoders and genetic algorithms
The use of machine learning in aerodynamic shape optimization problems has significantly increased in recent years. While existing deep learning...
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Interactive optimization of relation extraction via knowledge graph representation learning
Relation extraction is a vital task in constructing large-scale knowledge graphs, aiming to identify factual relations between entities from plain...
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Iterative alternating optimization of bi-orthogonal two-channel graph filter bank
The iterative alternating optimization (IAO) algorithm is proposed to optimize the coefficients of the (frequency domain/ spectral) general design...
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A crashworthiness optimization method of subway underframe structures based on the differential evolution of the weighted graph representation
The crashworthiness optimization of the subway end structure is essentially a topology–shape–size collaborative optimization problem of a composite...
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POSGO: an open-source software for GNSS pseudorange positioning based on graph optimization
Graph optimization (GO) can correlate more historical information to increase the resistance against the GNSS outliers. Therefore, GO has the...
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Mayfly Taylor Optimization-Based Graph Attention Network for Task Scheduling in Edge Computing
Multi-access edge computing (MEC) is a technology that enables devices with limited processing capabilities to handle computationally intensive tasks...
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Few-shot temporal knowledge graph completion based on meta-optimization
Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial...
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A two-stage entity event deduplication method based on graph node selection and node optimization strategy
Entity event deduplication is the task of identifying all duplication entity events that have described the same entity within a set of events....
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Enhancing fairness of trading environment: discovering overlap** spammer groups with dynamic co-review graph optimization
Within the thriving e-commerce landscape, some unscrupulous merchants hire spammer groups to post misleading reviews or ratings, aiming to manipulate...
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Leveraging Transfer Learning for Enhancing Graph Optimization Problem Solving
Reinforcement learning to solve graph optimization problems has attracted increasing attention recently. Typically, these models require extensive...
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Combinatorial optimization with physics-inspired graph neural networks
Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at...
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Intrusion detection using graph neural network and Lyapunov optimization in wireless sensor network
Sensor nodes deployed in a remote location are vulnerable to various attack. An intruder can easily capture and tamper with sensor nodes deployed in...
