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Intensifying graph diffusion-based salient object detection with sparse graph weighting
Salient object detection based on the diffusion process on the graph has achieved considerable performance. It mainly depends on the affinity matrix...
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Object tracking using fuzzy-based improved graph, interesting patches and multi-label MRF optimization
The complete object-tracking system typically comprises four phases. In this paper, we aim to focus on the first three phases. Graphs are often used...
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Multi-graph embedding for partial label learning
Partial label learning (PLL) is an essential weakly supervised learning method. In PLL, the example’s ground-truth label is unknown and hidden in a...
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Image steganalysis using modified graph clustering based ant colony optimization and Random Forest
In this paper, a steganalysis algorithm is proposed based on Modified Graph Clustering Based Ant Colony Optimization (MGCACO) feature selection and...
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Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm
Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
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Graph foundation model
Graph Foundation Models represent an evolving direction in graph machine learning. Drawing inspiration from the success of Large Language Models in...
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A Sparse Matrix Optimization Method for Graph Neural Networks Training
Graph neural networks (GNN) have shown great application potential in scientific research applications, biomedicine, and other fields, which exhibit...
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FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism
Traditional knowledge graph completion mainly focuses on static knowledge graph. Although there are efforts studying temporal knowledge graph...
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Graph Optimization Problems and Algorithms for DAG-Type Blockchains
The scalability of a blockchain is an issue when applying blockchain techniques to the management of mobility history and payment information. One...
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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications
Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual...
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PCGC: a performance compact graph compiler based on multilevel fusion-splitting rules
The existing deep learning compilers are unable to perform efficient hardware performance-related graph fusion when both time and power consumption...
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Investigation of graph-based clustering approaches along with graph neural networks for modeling armed conflict in Bangladesh
Determining fatality rates—a critical component of conflict analysis and comprehending the dynamics of armed conflict in Bangladesh are the main...
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Many-objective emergency aided decision making based on knowledge graph
After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However,...
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Multimodal optimization by particle swarm optimization with graph-based speciation using \(\beta\)-relaxed relative neighborhood graph and seed-centered mutation
Multimodal optimization is a very difficult task to search for all optimal solutions at once in optimization problems with multiple optimal...
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Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks
Text classification is a fundamental task in Text Mining (TM) with applications ranging from spam detection to sentiment analysis. One of the current...
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Joint learning of graph and latent representation for unsupervised feature selection
Data samples in real-world applications are not only related to high-dimensional features, but also related to each other. To fully exploit the...
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Combinatorial Optimization 8th International Symposium, ISCO 2024, La Laguna, Tenerife, Spain, May 22–24, 2024, Revised Selected Papers
This book constitutes the refereed proceedings of the 8th International Symposium on Combinatorial Optimization, ISCO 2024, held in La Laguna,...
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Adaptive curvature exploration geometric graph neural network
Graph Neural networks (GNNs) which are powerful and widely applied models are based on the assumption that graph topologies play key roles in the...
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Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
