Simplifying continuous-time quantum walks on dynamic graphs
Sign Up to like & getrecommendations! Published in 2022 at "Quantum Information Processing"
DOI: 10.1007/s11128-021-03403-7
Abstract: A continuous-time quantum walk on a dynamic graph evolves by Schrödinger’s equation with a sequence of Hamiltonians encoding the edges of the graph. This process is universal for quantum computing, but in general, the dynamic… read more here.
Keywords: dynamic graphs; dynamic graph; time quantum; continuous time ... See more keywords
An Efficient Snapshot Strategy for Dynamic Graph Storage Systems to Support Historical Queries
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DOI: 10.1109/access.2020.2994242
Abstract: Large-scale dynamic graphs typically involve big data. Recently a dynamic graph storage system is required to be capable of recreating any historical state to support historical queries. A typical storage solution supporting historical queries is… read more here.
Keywords: support historical; historical queries; graph storage; strategy ... See more keywords
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3082932
Abstract: Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but… read more here.
Keywords: dynamic networks; neural networks; graph neural; survey ... See more keywords
Keypoint Matching for Point Cloud Registration Using Multiplex Dynamic Graph Attention Networks
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2021.3097275
Abstract: The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a… read more here.
Keywords: keypoint matching; dynamic graph; attention; graph ... See more keywords
Dynamic Graph Optimization and Performance Evaluation for Delay-Tolerant Aeronautical Ad Hoc Network
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2021.3085898
Abstract: With the growing interest in providing internet access and cellular connectivity in the commercial aircraft, the compatibility between Air-to-Air (A2A) communications and current Air-to-Ground (A2G) macro-cellular communications is necessary to form an Aeronautical Ad hoc… read more here.
Keywords: aeronautical hoc; delay; transmission; hoc network ... See more keywords
A Fast Dynamic Graph Convolutional Network and CNN Parallel Network for Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3179419
Abstract: Deep learning has achieved impressive results on hyperspectral images (HSIs) classification. Among them, both convolutional neural networks (CNNs) and graph neural networks (GNNs) have great potential for hyperspectral image classification. Supervised CNNs can efficiently extract… read more here.
Keywords: network; classification; dynamic graph; image classification ... See more keywords
DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3185527
Abstract: Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged… read more here.
Keywords: graph convolutional; dynamic graph; graph; efficient dynamic ... See more keywords
Dynamic Graph Message Passing Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3207500
Abstract: Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically… read more here.
Keywords: message; dynamic graph; message passing; passing networks ... See more keywords
Event-Based Dynamic Graph Visualisation
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DOI: 10.1109/tvcg.2018.2886901
Abstract: Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed algorithms can exploit to compute a layout. However, often dynamic graphs are expressed as a series of events where the nodes… read more here.
Keywords: time; event based; based dynamic; dynamic graphs ... See more keywords
Event‐based Dynamic Graph Drawing without the Agonizing Pain
Sign Up to like & getrecommendations! Published in 2022 at "Computer Graphics Forum"
DOI: 10.1111/cgf.14615
Abstract: Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a… read more here.
Keywords: dynamic graph; event; graph drawing; event based ... See more keywords
Comparing the Efficacy of Static and Dynamic Graph Types in Communicating Complex Statistical Relationships
Sign Up to like & getrecommendations! Published in 2019 at "Frontiers in Psychology"
DOI: 10.3389/fpsyg.2019.02986
Abstract: Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure’s construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to… read more here.
Keywords: dynamic graph; efficacy; static dynamic; graph ... See more keywords