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Graph Random Neural Features for Distance-Preserving Graph Representations
[article]
2020
arXiv
pre-print
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We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. ...
The embedding naturally deals with graph isomorphism and preserves the metric structure of the graph domain, in probability. ...
Acknowledgements This research is funded by the Swiss National Science Foundation project 200021 172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems." The work of L. ...
arXiv:1909.03790v3
fatcat:2eink7sbbrhhdjsmnuszm3kd7e
Fast Sequence-Based Embedding with Diffusion Graphs
[article]
2020
arXiv
pre-print
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Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. ...
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. ...
In particular, it scales better with increasing density (vertex degrees) of graphs. Our experiments also show that the embedding preserves graph distances to a high accuracy. ...
arXiv:2001.07463v1
fatcat:ngjsvc3s3fezfndhuadcl5hzoa
Fast Sequence-Based Embedding with Diffusion Graphs
[chapter]
2018
Complex Networks IX
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Vertex sequence based embedding procedures use features extracted from linear sequences of vertices to create embeddings using a neural network. ...
A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves properties such as distances between nodes. ...
) Learning an embedding from the features. ...
doi:10.1007/978-3-319-73198-8_9
fatcat:mbg4ctrzhrg75etcqdi676r4tu
Survey of network embedding techniques for social networks
2019
Turkish Journal of Electrical Engineering and Computer Sciences
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This required use of kernel functions or graph statistics making the entire exercise task-dependent. NRL frameworks eliminated the need for feature engineering and made SNA task-independent. ...
Due to the scientific interest in this domain there has been a mushrooming of embedding techniques. ...
Random walk cooccurrence preserving techniques These techniques perform random walks on the graph starting from different nodes in the network. ...
doi:10.3906/elk-1807-333
fatcat:dugob6cllja5jmtdj63a2hzm7i
Graph representation learning: a survey
2020
APSIPA Transactions on Signal and Information Processing
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Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. ...
In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. ...
It preserves spatial distances. ...
doi:10.1017/atsip.2020.13
fatcat:lirq3kp25jfilgkf66u2rlkhky
A Tutorial on Network Embeddings
[article]
2018
arXiv
pre-print
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These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. ...
We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. ...
Local Linear Embeddings (LLE) -Unlike MDS, which preserves pairwise distances between feature vectors, LLE [39] only exploits the local neighborhood of data points and does not attempt to estimate distance ...
arXiv:1808.02590v1
fatcat:ramuqdavczfabb4o7r42kice7q
Graph Representation Learning: A Survey
[article]
2019
arXiv
pre-print
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Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. ...
In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. ...
It preserves spatial distances. ...
arXiv:1909.00958v1
fatcat:6wbxy5jjx5ditbiiviwbuqyww4
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:2005.03675v1 fatcat:2nwx74fymzacvka2qq5lunoo3uOther Versions
Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. ...
The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure. ...
in a multi-step pipeline where random walks are first generated from the graph and then used to learn embeddings. ...
arXiv:2005.03675v3
fatcat:6eoicgprdvfbze732nsmpaumqe
Network Representation Learning: From Traditional Feature Learning to Deep Learning
2020
IEEE Access
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The transductive graph embedding is applied for predicting class label and graph context based on the input feature of observed labeled data and embeddings extracted from graph structure. ...
Node features can be obtained from the summation of neighbor features, so that the algorithm has the ability to preserve the locally linear structure of neighborhood. ...
doi:10.1109/access.2020.3037118
fatcat:kca6htfarjdjpmtwcvbsppfzui
Survey on graph embeddings and their applications to machine learning problems on graphs
2021
PeerJ Computer Science
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So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. ...
First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. ...
IsoMap and LLE were proposed to model global structure while preserving local distances or sampling from the local neighborhood of nodes. ...
doi:10.7717/peerj-cs.357
pmid:33817007
pmcid:PMC7959646
fatcat:ntronyrbgfbedez5dks6h4hoq4
Isometric Graph Neural Networks
[article]
2020
arXiv
pre-print
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that the learned embeddings do account for graph distances. ...
Geometric techniques to extract such representations have poor scaling over large graph size, and recent advances in Graph Neural Network (GNN) algorithms have limited ability to reflect graph distance ...
Therefore, learning an embedding from such representation of the features may not preserve the distances in the graph. ...
arXiv:2006.09554v1
fatcat:jytiaunwt5aqhaeuj4u5o6y42e
Semantic preserving embeddings for multi-relational graphs
2017
2017 Computing Conference
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It shows how vector representations that maintain semantic and topological features of the original data can be obtained from neural encoding architectures and considering the topological properties of ...
the graph. ...
In this way, we are interested in finding embeddings that can reflect, within the vector space features (distance, linearity, clustering, etc.) some semantic features of the original graph. ...
doi:10.1109/sai.2017.8252079
fatcat:dvu6u7jvl5fqjdar4nvgdioz4m
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
[article]
2020
arXiv
pre-print
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Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such ...
baseline methods, and its effectiveness in capturing and preserving the community structure of graphs. ...
Graph Neural Network Graph Neural Network (GNN) is an effective messagepassing architecture for embedding the graph nodes and their local structures. ...
arXiv:2012.05980v1
fatcat:toelhmu3mjb6ngtn3nnb56fz7e
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
[article]
2019
arXiv
pre-print
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Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated ...
We propose the use of graph neural networks for QSOR, and show they significantly out-perform prior methods on a novel data set labeled by olfactory experts. ...
Each Graph Neural Network (GNN) layer, here represented as different colors, transforms the features from the previous layer. ...
arXiv:1910.10685v2
fatcat:2smeyl4bargkvfjx5jelubgwbi
Learning Graph-Level Representations with Recurrent Neural Networks
[article]
2018
arXiv
pre-print
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Graph nodes are mapped into node sequences sampled from random walk approaches approximated by the Gumbel-Softmax distribution. ...
The majority of these methods start by embedding the graph nodes into a low-dimensional vector space, followed by using some scheme to aggregate the node embeddings. ...
Figure 1 : Graph recurrent neural network model to learn graph-level representations. Step 1: Node embeddings are learned from the graph structures and node features over the entire training samples. ...
arXiv:1805.07683v4
fatcat:lsrbrfswtjejzcdbtggm77sa7y
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