Results 31 to 40 of about 170,883 (277)
Graph sampling for node embedding
CoRR, 2022 Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector, which aim to extract useful information from both the eigenvectors ...
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Weighted Spectral Embedding of Graphs [PDF]
, 2018 We present a novel spectral embedding of graphs that incorporates weights assigned to the nodes, quantifying their relative importance. This spectral embedding is based on the first eigenvectors of some properly normalized version of the Laplacian.
Bonald, Thomas +2 more
core +2 more sources
A Node Embedding-Based Influential Spreaders Identification Approach
Mathematics, 2020 Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node ...
Dongming Chen +4 more
doaj +1 more source
Virtual Network Embedding Based on Topology Potential
Entropy, 2018 To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer ...
Xinbo Liu, Buhong Wang, Zhixian Yang
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GAE-Based Document Embedding Method for Clustering
IEEE Access, 2022 Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent ...
Sungwon Jung, Sangmin Ka
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Embedding cube-connected cycles graphs into faulty hypercubes [PDF]
, 1994 We consider the problem of embedding a cube-connected cycles graph (CCC) into a hypercube with edge faults. Our main result is an algorithm that, given a list of faulty edges, computes an embedding of the CCC that spans all of the nodes and avoids all of
Bruck, Jehoshua +2 more
core +1 more source
div2vec: Diversity-Emphasized Node Embedding [PDF]
CoRR, 2020 Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but in real-world systems, the recommendation diversity should be considered as well to improve user experiences. In this paper, we propose the diversity-emphasized node embedding
Jisu Jeong +5 more
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On distributed virtual network embedding with guarantees [PDF]
, 2016 To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET)
Di Paola, Donato +2 more
core +1 more source
ALPINE : Active Link Prediction using Network Embedding [PDF]
, 2020 Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime ...
Chen, Xi +3 more
core +1 more source
Enhancing Attributed Network Embedding via Similarity Measure
IEEE Access, 2019 Network embedding aims to represent network structural and attributed information with low-dimensional vectors, which has been demonstrated to be beneficial for many network analysis tasks, such as link prediction, node classification and visualization ...
Bin Yu +4 more
doaj +1 more source