Results 11 to 20 of about 170,883 (277)
Node Embedding over Temporal Graphs [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different
Guy, Ido, Radinsky, Kira, Singer, Uriel
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Multi-Scale attributed node embedding [PDF]
Journal of Complex Networks, 2021 Abstract We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighbourhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale ...
Benedek Rozemberczki +2 more
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Hyperbolic node embedding for signed networks [PDF]
Neurocomputing, 2021 Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces.
Wenzhuo Song +4 more
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An Impossibility Theorem for Node Embedding
CoRR, 2021 With the increasing popularity of graph-based methods for dimensionality reduction and representation learning, node embedding functions have become important objects of study in the literature. In this paper, we take an axiomatic approach to understanding node embedding methods, first stating three properties for embedding dissimilarity networks, then
T. Mitchell Roddenberry +2 more
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Multiplex network infomax: Multiplex network embedding via information fusion
Digital Communications and Networks, 2023 For networking of big data applications, an essential issue is how to represent networks in vector space for further mining and analysis tasks, e.g., node classification, clustering, link prediction, and visualization.
Qiang Wang +5 more
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Frontiers in Physics, 2022 Node classification, as a central task in the graph data analysis, has been studied extensively with network embedding technique for single-layer graph network. However, there are some obstacles when extending the single-layer network embedding technique
Beibei Han +4 more
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Social Network Embedding Method Combining Node Attributes and Loop-Free Path [PDF]
Jisuanji kexue yu tansuo, 2022 Network embedding’s goal is to learn the low-dimensional node feature representation in the network. The learned features are used in various network analysis tasks, such as node classification, link prediction, community detection and recommendation ...
WANG Benyu, GU Yijun, PENG Shufan
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Evaluating node embeddings of complex networks
Journal of Complex Networks, 2022 Abstract Graph embedding is a transformation of nodes of a graph into a set of vectors. A good embedding should capture the graph topology, node-to-node relationship and other relevant information about the graph, its subgraphs and nodes.
Arash Dehghan-Kooshkghazi +4 more
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Node embeddings in dynamic graphs [PDF]
Applied Network Science, 2019 Abstract In this paper, we present algorithms that learn and update temporal node embeddings on the fly for tracking and measuring node similarity over time in graph streams. Recently, several representation learning methods have been proposed that are capable of embedding nodes in a vector space in a way that captures the network structure.
Ferenc Béres +3 more
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Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder
IEEE Access, 2022 Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications.
Amr Thabit Al-Furas +3 more
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