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   Articles with "bipartite graph" as a keyword



BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae045

Abstract: Abstract Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field… read more here.

Keywords: bipartite graph; graph attention; single slice; slice ... See more keywords

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3228957

Abstract: The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers’ characteristics, such as educational background, degree, professional title, age, teaching age,… read more here.

Keywords: weighted bipartite; quality; bipartite graph; course ... See more keywords

BGCFI: Efficient Verification in Fine-Grained Control-Flow Integrity Based on Bipartite Graph

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recommendations! Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2023.3234184

Abstract: Control-flow integrity (CFI) is considered a principled mitigation against control-flow hijacking even under the most powerful attacker who can arbitrarily write and read memory. However, existing schemes still demonstrated limitations in either guaranteeing high security… read more here.

Keywords: bipartite graph; fine grained; control flow;

Fuzzy Embedded Clustering Based on Bipartite Graph for Large-Scale Hyperspectral Image

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recommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2021.3073035

Abstract: Hyperspectral image (HSI) clustering has been widely used in the field of remote sensing. However, most traditional clustering algorithms are not suitable for dealing with large-scale HSI due to their low clustering performance and high… read more here.

Keywords: large scale; fuzzy clustering; hyperspectral image; based bipartite ... See more keywords

Graph Simplification-Aided ADMM for Decentralized Composite Optimization

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recommendations! Published in 2021 at "IEEE Transactions on Cybernetics"

DOI: 10.1109/tcyb.2019.2953538

Abstract: In this article, we consider the problem of decentralized composite optimization over a connected and symmetric graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes… read more here.

Keywords: simplest bipartite; composite optimization; decentralized composite; graph ... See more keywords

Learning an Optimal Bipartite Graph for Subspace Clustering via Constrained Laplacian Rank.

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recommendations! Published in 2021 at "IEEE transactions on cybernetics"

DOI: 10.1109/tcyb.2021.3113520

Abstract: In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering… read more here.

Keywords: laplacian rank; bipartite graph; graph; subspace clustering ... See more keywords

A Bipartite Graph Partition-Based Coclustering Approach With Graph Nonnegative Matrix Factorization for Large Hyperspectral Images

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recommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2021.3097358

Abstract: Clustering large hyperspectral images (HSIs) is a very challenging problem because large HSIs have high dimensionality, large spectral variability, and large computational and memory consumption. Recently, sparse subspace clustering (SSC) has achieved remarkable success in… read more here.

Keywords: matrix; graph partition; graph; hsis ... See more keywords

Multiview Spectral Clustering With Bipartite Graph

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recommendations! Published in 2022 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2022.3171411

Abstract: Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction… read more here.

Keywords: bipartite graph; graph; complementary information; spectral clustering ... See more keywords

Co-Clustering on Bipartite Graphs for Robust Model Fitting

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recommendations! Published in 2022 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2022.3214073

Abstract: Recently, graph-based methods have been widely applied to model fitting. However, in these methods, association information is invariably lost when data points and model hypotheses are mapped to the graph domain. In this paper, we… read more here.

Keywords: bipartite graph; bipartite graphs; clustering bipartite; model ... See more keywords

Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3425891

Abstract: Searching on bipartite graphs serves as a fundamental task for various real-world applications, such as recommendation systems, database retrieval, and document querying. Conventional approaches rely on similarity matching in continuous euclidean space of vectorized node… read more here.

Keywords: search; graph contrastive; effective top; bipartite graph ... See more keywords

L-BGNN: Layerwise Trained Bipartite Graph Neural Networks.

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3171199

Abstract: Learning low-dimensional representations of bipartite graphs enables e-commerce applications, such as recommendation, classification, and link prediction. A layerwise-trained bipartite graph neural network (L-BGNN) embedding method, which is unsupervised, efficient, and scalable, is proposed in this… read more here.

Keywords: bipartite graph; trained bipartite; graph neural; layerwise ... See more keywords