Results 101 to 110 of about 56,309 (276)
Analysis of quantum error correction with symmetric hypergraph states
, 2018 Graph states have been used to construct quantum error correction codes for independent errors. Hypergraph states generalize graph states, and symmetric hypergraph states have been shown to allow for the correction of correlated errors. In this paper, it
Bruß, Dagmar +2 more
core +1 more source
Cross‐Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Cross‐domain graph anomaly detection (CD‐GAD) is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph. CD‐GAD classifies anomalies as unique or common based on their presence in both the source and target graphs.
Changqin Huang +6 more
wiley +1 more source
Generalized Hypergraph Coloring
Discussiones Mathematicae Graph Theory, 2021 A smooth hypergraph property 𝒫 is a class of hypergraphs that is hereditary and non-trivial, i.e., closed under induced subhypergraphs and it contains a non-empty hypergraph but not all hypergraphs.
Schweser Thomas
doaj +1 more source
Intelligent Medical Diagnosis Model Based on Graph Neural Networks for Medical Images
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Recently, numerous estimation issues have been solved due to the developments in data‐driven artificial neural networks (ANN) and graph neural networks (GNN). The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format.
Ashutosh Sharma, Amit Sharma, Kai Guo
wiley +1 more source
Rainbow connection numbers of some classes of s-overlapping r-uniform hypertrees with size t
AIMS MathematicsThe rainbow connection concept was developed to determine the minimum number of passwords required to exchange encrypted information between two agents. If the information exchange involves divisions managing more than two agents, the rainbow connection ...
Sitta Alief Farihati +2 more
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A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images
Remote Sensing, 2023 The very high spatial resolution (VHR) remote sensing images have been an extremely valuable source for monitoring changes occurring on the Earth’s surface. However, precisely detecting relevant changes in VHR images still remains a challenge, due to the
Junzheng Wu +6 more
doaj +1 more source
State‐of‐the‐art Report in Sketch Processing
Computer Graphics Forum, EarlyView.Abstract Sketches are a powerful and natural form of communication and are used in numerous systems for modelling, animation, shape retrieval, and editing. Despite their popularity, rough sketches — whether raster or vector, 2D or 3D — are often too complex and imprecise to be used directly and thus need special processing.
Chenxi Liu, Mikhail Bessmeltsev
wiley +1 more source
Clustering Hypergraphs via the MapEquation
IEEE Access, 2021 A hypergraph is a generalization of a graph in that the restriction of pairwise affinity scores is lifted in favor of affinity scores that can be evaluated between an arbitrary number of inputs.
Matthew Swan, Justin Zhan
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Hypergraph Learning with Line Expansion
, 2020 Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Abdelzaher, Tarek +3 more
core
Optimizing Staircase Motifs in Biofabric Network Layouts
Computer Graphics Forum, EarlyView.Abstract Biofabric is a novel method for network visualization, with promising potential to highlight specific network features. Recent studies emphasize the importance of staircase motifs — equivalent to fans or stars in node‐link diagrams — within Biofabric.
Sara Di Bartolomeo +2 more
wiley +1 more source