Results 51 to 60 of about 184,942 (303)
Time-varying graph representation learning via higher-order skip-gram with negative sampling
EPJ Data Science, 2022 Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms.
Simone Piaggesi, André Panisson
doaj +1 more source
Improving the Robustness of GraphSAINT via Stability Training
ParadigmPlus, 2021 Graph Neural Networks (GNNs) field has a dramatic development nowadays due to the strong representation capabilities for data in non-Euclidean space, such as graph data.
Yuying Wang, Huixuan Chi, Qinfen Hao
doaj +1 more source
Node copying: A random graph model for effective graph sampling
Signal Processing, 2022 There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account ...
Florence Regol +5 more
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FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
Sampling Graph Signals with Sparse Dictionary Representation
, 2021 Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often ...
Isufi, E. (author) +2 more
core +1 more source
Local Differential Privacy Graph Data Modeling Method for Link Prediction
Journal of Harbin University of Science and Technology, 2023 To solve the problem of node sensitive link privacy being exposed in the process of link prediction on industrial business graph data , according to the theory of local differential privacy , the shortcomings of the existing graph privacy protection ...
HANQilong, WUXiaoming
doaj +1 more source
Weighted Edge Sampling for Static Graphs
International Journal of Data Mining, Modelling and Management, 2023 Graph Sampling provides an efficient yet inexpensive solution for analyzing large graphs. While extracting small representative subgraphs from large graphs, the challenge is to capture the properties of the original graph. Several sampling algorithms have been proposed in previous studies, but they lack in extracting good samples.
Muhammad Irfan Yousuf, Raheel Anwar
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FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
Graph Vertex Sampling with Arbitrary Graph Signal Hilbert Spaces
, 2020 International audienceGraph vertex sampling set selection aims at selecting a set of ver-tices of a graph such that the space of graph signals that can be reconstructed exactly from those samples alone is maximal.
Antonio Ortega +5 more
core +1 more source
Empirical Characterization of Graph Sampling Algorithms
Social Network Analysis and Mining, 2022 Abstract Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including ...
Muhammad Irfan Yousuf +2 more
openaire +2 more sources