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Class Representative Computation Using Graph Embedding [chapter]

Fahri Aydos, Ahmet Soran, M. Fatih Demirci
2013 Lecture Notes in Computer Science  
In this paper we propose an algorithm which represents the graphs belonging to a particular set as points through graph embedding and operates in the vector space to compute the representative of the set  ...  We use the k-means clustering algorithm to learn centroids forming the representatives.  ...  The k-means clustering algorithm is then used to learn the centroids, which forms the class representatives (transitions 4 and 5).  ... 
doi:10.1007/978-3-642-41181-6_46 fatcat:ada2bamv5zd2tfnm465exw72ja

Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph [chapter]

M. Ferrer, E. Valveny, F. Serratosa, I. Bardají, H. Bunke
2009 Lecture Notes in Computer Science  
We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters.  ...  In the graph-based k -means algorithm, the centers of the clusters have been traditionally represented using the set median graph.  ...  Acknowledgements This work has been supported by the Spanish research programmes Consolider Ingenio 2010 CSD2007-00018, TIN2006-15694-C02-02 and TIN2008-04998.  ... 
doi:10.1007/978-3-642-03767-2_42 fatcat:uapdelz7fjgmre6jpgfksfzouu

Prototype Selection for Graph Embedding Using Instance Selection [chapter]

Magdiel Jiménez-Guarneros, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad
2015 Lecture Notes in Computer Science  
In this paper, we evaluate the use of an instance selection method based on clustering for graph embedding, which selects border prototypes and some non-border prototypes.  ...  Currently, graph embedding has taken a great interest in the area of structural pattern recognition, especially techniques based on representation via dissimilarity.  ...  This work was partly supported by the National Council of Science and Technology of Mexico (CONACyT) through the project grant CB2008-106366; and the scholarship grant 298513.  ... 
doi:10.1007/978-3-319-19264-2_9 fatcat:pyxqlxrnpbe5jiiv4tlbm37zfy

Unsupervised Network Embedding for Graph Visualization, Clustering and Classification [article]

Leonardo Gutiérrez-Gómez, Jean-Charles Delvenne
2019 arXiv   pre-print
In this work we provide an unsupervised approach to learn embedding representation for a collection of graphs so that it can be used in numerous graph mining tasks.  ...  Results reveal that our method outperforms well known graph distances and graph-kernels in clustering and classification tasks, being highly efficient in runtime.  ...  From a given family of graphs, their embeddings are learned and used to compute the embedding distance matrix (Eq. 6).  ... 
arXiv:1903.05980v2 fatcat:sa7p47a3ynaizbh3ynilo7cx7y

Tackling Provably Hard Representative Selection via Graph Neural Networks [article]

Seyed Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni
2022 arXiv   pre-print
In this paper, we focus on finding representatives that optimize the accuracy of a model trained on the selected representatives. We study RS for data represented as attributed graphs.  ...  Representative selection (RS) is the problem of finding a small subset of exemplars from an unlabeled dataset, and has numerous applications in summarization, active learning, data compression and many  ...  For both models, we create k clusters, use the (hard) cluster assignments to compute cluster centers, and select the closest point to each cluster center as a representative.  ... 
arXiv:2205.10403v1 fatcat:5gtyjjcxozhjjlgurggg4tyizm

Discovering alignment relations with Graph Convolutional Networks: a biomedical case study [article]

Pierre Monnin and Chedy Raïssi and Amedeo Napoli and Adrien Coulet
2021 arXiv   pre-print
and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.  ...  considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.  ...  Acknowledgments This work was supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant ANR15-CE23-0028, and by the Snowball Inria Associate Team.  ... 
arXiv:2011.06023v2 fatcat:bgncjhumdnfg5e3paydjrrhaje

Force2Vec: Parallel force-directed graph embedding [article]

Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
2020 arXiv   pre-print
We develop Force2Vec that uses force-directed graph layout models in a graph embedding setting with an aim to excel in both machine learning (ML) and visualization tasks.  ...  In comparison to existing methods, Force2Vec is better in graph visualization and performs comparably or better in ML tasks such as link prediction, node classification, and clustering.  ...  Graph embedding algorithms are computationally expensive.  ... 
arXiv:2009.10035v1 fatcat:bm4dieccxnctfm4qcisdon46py

Unsupervised network embeddings with node identity awareness

Leonardo Gutiérrez-Gómez, Jean-Charles Delvenne
2019 Applied Network Science  
In this work we provide an unsupervised approach to learn graph embeddings for a collection of graphs defined on the same set of nodes so that it can be used in numerous graph mining tasks.  ...  Results reveal that our method outperforms well known graph distances and graph-kernels in clustering and classification tasks, being highly efficient in runtime.  ...  Acknowledgements We thank Leto Peel and Michel Fanuel for helpful discussion and suggestions.  ... 
doi:10.1007/s41109-019-0197-1 fatcat:hbmoewunsnfsxiary2dihtayqu

Learning Multi-layer Graphs and a Common Representation for Clustering [article]

Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
2021 arXiv   pre-print
We can explain interactions between the entities in multi-view data using a multi-layer graph with a common vertex set, which represents the shared entities.  ...  Assuming a smoothness data model, we jointly estimate the graph Laplacian matrices of the individual graph layers and low-dimensional embedding of the common vertex set.  ...  The multi-view spectral clustering methods, SC-GED [15] and SC-ML [16] , obtain the common node embeddings by computing a joint spectrum via a generalized eigendecomposition and merging the node embeddings  ... 
arXiv:2010.12301v2 fatcat:uzxrk4yjz5eblnc4ob74wxnxem

Discovering alignment relations with Graph Convolutional Networks: A biomedical case study

Pierre Monnin, Chedy Raïssi, Amedeo Napoli, Adrien Coulet, Mehwish Alam, Davide Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero, Harald Sack, Mehwish Alam, Davide Buscaldi (+4 others)
2022 Semantic Web Journal  
and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.  ...  considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.  ...  Acknowledgements This work was supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant ANR15-CE23-0028, and by the Snowball Inria Associate Team.  ... 
doi:10.3233/sw-210452 fatcat:zfydoeta6ven3fefjvmqxw3qia

Fuzzy multilevel graph embedding

Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep Lladós, Thierry Brouard
2013 Pattern Recognition  
Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition.  ...  The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector.  ...  We have used the actual number of classes in dataset (Table 1 ) as the number of clusters to be found by k-means clustering.  ... 
doi:10.1016/j.patcog.2012.07.029 fatcat:kn2i2vxjpjh6tp5mksv4inxldm

Bags of Graphs for Human Action Recognition [chapter]

Xavier Cortés, Donatello Conte, Hubert Cardot
2018 Lecture Notes in Computer Science  
In this paper we propose to improve the representativeness of this model including the structural relations between the interest points using graph sequences.  ...  The proposed model achieves very competitive results for human action recognition and could also be applied to solve graph sequences classification problems.  ...  The values of the embedded vector are filled by taking the GED between the graphs we are embedding to each one of the graphs in the set we are clustering.  ... 
doi:10.1007/978-3-319-97785-0_41 fatcat:5phtykjapzakrad2yniaesxqqi

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

Jiaming Wang, Xiaolan Xie, Xiaochun Cheng, Yuhan Wang
2022 Computer systems science and engineering  
Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning.  ...  class clusters of different shapes.  ...  datasets. the Computer dataset is derived from Amazon computer product information purchase information, where the nodes represent computer goods information and the edges represent goods often sold together  ... 
doi:10.32604/csse.2022.027005 fatcat:fhipnu7ayvfl7ksec6mpfcsiwe

Isotree: Tree clustering via metric embedding

Bai Xiao, Andrea Torsello, Edwin R. Hancock
2008 Neurocomputing  
The spectrum of the Laplacian matrix for the embedded graphs may be used for purposes of comparing trees and for clustering them.  ...  As a result, structurally distinct trees possess degenerate graph-spectra, and spectral methods can be reliably used to neither compute distances between trees nor to cluster trees.  ...  The spectrum of the Laplacian matrix for the embedded graphs may be used for purposes of comparing trees and for clustering them.  ... 
doi:10.1016/j.neucom.2007.11.033 fatcat:h2jsw7j7cvfafbav7pxndnnhvu

Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer [chapter]

Anant Madabhushi, Jianbo Shi, Mark Rosen, John E. Tomaszeweski, Michael D. Feldman
2005 Lecture Notes in Computer Science  
We also present a novel way of visualizing the class embeddings which makes it easy to appreciate inter-class relationships and to infer the presence of new classes which were not part of the original  ...  In this paper we present a novel application of graph embedding in improving the accuracy of supervised classification schemes, especially in cases where object class labels cannot be reliably ascertained  ...  We borrow a graph embedding technique used in the computer vision domain [4] for improving classification accuracy and for novel class detection.  ... 
doi:10.1007/11566465_90 fatcat:xzhkd6qejbgmtfj43jvfzyaig4
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