Search
Search Results
-
Modified graph systems for distributed optimization
In distributed optimization theory, network topology graphs are important in communications among multiple agents. However, distributed optimization...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
Greedy optimization of resistance-based graph robustness with global and local edge insertions
The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems...
-
Deep deterministic policy gradient and graph attention network for geometry optimization of latticed shells
AbstractThis paper proposes a combined approach of deep deterministic policy gradient (DDPG) and graph attention network (GAT) to the geometry...
-
Pruning rate-controlled filter order–information structure similarity graph clustering for DCNN structure optimization methods
Filter pruning is a compression and acceleration method for deep convolutional neural network models that operates at a large scale. Many researchers...
-
Learning to solve graph metric dimension problem based on graph contrastive learning
Deep learning has been widely used to solve graph and combinatorial optimization problems. However, proper model deployment is critical for training...
-
Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors
Graph-based dimensionality reduction methods have attracted much attention for they can be applied successfully in many practical problems such as...
-
Consensus Affinity Graph Learning via Structure Graph Fusion and Block Diagonal Representation for Multiview Clustering
Learning a robust affinity graph is fundamental to graph-based clustering methods. However, some existing affinity graph learning methods have...
-
A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
-
Heterogeneous graph neural network with graph-data augmentation and adaptive denoising
Heterogeneous graphs are especially important in our daily life, which describe objects and their connections through nodes and edges. For this...
-
Multi-person pose estimation based on graph grou** optimization
Multi-person pose estimation has been an increasingly popular topic with the advancements of all kinds of computer vision and human-machine...
-
Cardinality estimation for property graph queries with gated learning approach on the graph database
With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the...
