Results 41 to 50 of about 4,728,201 (299)
Conditional Network Embeddings
CoRR, 2018 Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such that `similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if `similar' means being `more likely to be connected') or classification (if `similar' means `
Kang, Bo, Lijffijt, Jefrey, De Bie, Tijl
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Zoo guide to network embedding
Journal of Physics: Complexity, 2023 Abstract Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has ...
Baptista, A +3 more
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Network Embedding: An Overview
CoRR, 2019 Networks are one of the most powerful structures for modeling problems in the real world. Downstream machine learning tasks defined on networks have the potential to solve a variety of problems. With link prediction, for instance, one can predict whether two persons will become friends on a social network.
Nino Arsov, Georgina Mirceva
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Gaussian Embedding of Temporal Networks
IEEE Access, 2023 Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuous-time relational data with timestamped interactions introduces unique challenges due to its sparsity.
Raphaël Romero +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role ...
Quanyu Dai +3 more
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Compositional Network Embedding
CoRR, 2019 Accepted By RecSys ...
Tianshu Lyu +4 more
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Generating Virtual Network Embedding Problems with Guaranteed Solutions [PDF]
, 2016 The efficiency of network virtualization depends on the appropriate assignment of resources. The underlying problem, called Virtual Network Embedding, has been much discussed in the literature, and many algorithms have been proposed, attempting to ...
Fischer, Andreas, +5 more
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Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism
, 2021 Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks.
Jing, Xin +7 more
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Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization [PDF]
, 2012 In many areas of research, complex signals are commonly represented by high dimensional feature vectors. However, high dimensional vectors are difficult to analyze and interpret due to the curse of dimensionality.
Lunga, Dalton, Erosy, Okan
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Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems
IEEE Access, 2021 Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers'
Ali Akbar Nasiri +2 more
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