Relevant bibliographies by topics / Graph classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Graph classification"

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Liu, Xien, Xinxin You, Xiao Zhang, Ji Wu, and Ping Lv. "Tensor Graph Convolutional Networks for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8409–16. http://dx.doi.org/10.1609/aaai.v34i05.6359.

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Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from nei
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RIESEN, KASPAR, and HORST BUNKE. "GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1053–81. http://dx.doi.org/10.1142/s021800140900748x.

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Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propos
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Aref'ev, Roman D., John T. Baldwin та Marco Mazzucco. "Classification of δ-invariant amalgamation classes". Journal of Symbolic Logic 64, № 4 (1999): 1743–50. http://dx.doi.org/10.2307/2586809.

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Hrushovski's generalization of the Fraisse construction has provided a rich source of examples in model theory, model theoretic algebra and random graph theory. The construction assigns to a dimension function δ and a class K of finite (finitely generated) models a countable ‘generic’ structure. We investigate here some of the simplest possible cases of this construction. The class K will be a class of finite graphs; the dimension, δ(A), of a finite graph A will be the cardinality of A minus the number of edges of A. Finally and significantly we restrict to classes which are δ-invariant. A cla
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Gumbrell, Lee, and James McKee. "A classification of all 1-Salem graphs." LMS Journal of Computation and Mathematics 17, no. 1 (2014): 582–94. http://dx.doi.org/10.1112/s1461157014000060.

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AbstractOne way to study certain classes of polynomials is by considering examples that are attached to combinatorial objects. Any graph $G$ has an associated reciprocal polynomial $R_{G}$, and with two particular classes of reciprocal polynomials in mind one can ask the questions: (a) when is $R_{G}$ a product of cyclotomic polynomials (giving the cyclotomic graphs)? (b) when does $R_{G}$ have the minimal polynomial of a Salem number as its only non-cyclotomic factor (the non-trivial Salem graphs)? Cyclotomic graphs were classified by Smith (Combinatorial structures and their applications, Pr
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Bera, Abhijit, Mrinal Kanti Ghose, and Dibyendu Kumar Pal. "Graph Classification Using Back Propagation Learning Algorithms." International Journal of Systems and Software Security and Protection 11, no. 2 (2020): 1–12. http://dx.doi.org/10.4018/ijsssp.2020070101.

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Due to the propagation of graph data, there has been a sharp focus on developing effective methods for classifying the graph object. As most of the proposed graph classification techniques though effective are constrained by high computational overhead, there is a consistent effort to improve upon the existing classification algorithms in terms of higher accuracy and less computational time. In this paper, an attempt has been made to classify graphs by extracting various features and selecting the important features using feature selection algorithms. Since all the extracted graph-based featur
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Schmidt, Miriam, Günther Palm, and Friedhelm Schwenker. "Spectral graph features for the classification of graphs and graph sequences." Computational Statistics 29, no. 1-2 (2012): 65–80. http://dx.doi.org/10.1007/s00180-012-0381-6.

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Zhang, Yingxue, Soumyasundar Pal, Mark Coates, and Deniz Ustebay. "Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5829–36. http://dx.doi.org/10.1609/aaai.v33i01.33015829.

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Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spuri
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SCHENKER, ADAM, MARK LAST, HORST BUNKE, and ABRAHAM KANDEL. "CLASSIFICATION OF WEB DOCUMENTS USING GRAPH MATCHING." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 03 (2004): 475–96. http://dx.doi.org/10.1142/s0218001404003241.

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In this paper we describe a classification method that allows the use of graph-based representations of data instead of traditional vector-based representations. We compare the vector approach combined with the k-Nearest Neighbor (k-NN) algorithm to the graph-matching approach when classifying three different web document collections, using the leave-one-out approach for measuring classification accuracy. We also compare the performance of different graph distance measures as well as various document representations that utilize graphs. The results show the graph-based approach can outperform
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Eilers, Søren, Gunnar Restorff, Efren Ruiz, and Adam P. W. Sørensen. "Geometric Classification of Graph C*-algebras over Finite Graphs." Canadian Journal of Mathematics 70, no. 2 (2018): 294–353. http://dx.doi.org/10.4153/cjm-2017-016-7.

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AbstractWe address the classification problem for graph C*-algebras of finite graphs (finitely many edges and vertices), containing the class of Cuntz-Krieger algebras as a prominent special case. Contrasting earlier work, we do not assume that the graphs satisfy the standard condition (K), so that the graph C*-algebras may come with uncountably many ideals.We find that in this generality, stable isomorphism of graph C*-algebras does not coincide with the geometric notion of Cuntz move equivalence. However, adding a modest condition on the graphs, the two notions are proved to be mutually equi
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Magelinski, Thomas, David Beskow, and Kathleen M. Carley. "Graph-Hist: Graph Classification from Latent Feature Histograms with Application to Bot Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5134–41. http://dx.doi.org/10.1609/aaai.v34i04.5956.

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Neural networks are increasingly used for graph classification in a variety of contexts. Social media is a critical application area in this space, however the characteristics of social media graphs differ from those seen in most popular benchmark datasets. Social networks tend to be large and sparse, while benchmarks are small and dense. Classically, large and sparse networks are analyzed by studying the distribution of local properties. Inspired by this, we introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent local features, bins nodes together along 1-D cross sect
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Dissertations / Theses on the topic "Graph classification"

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Wainer, L. J. "Online graph-based learning for classification." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1446151/.

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The aim of this thesis is to develop online kernel based algorithms for learning clas sification functions over a graph. An important question in machine learning is: how to learn functions in a high dimension One of the benefits of using a graphical representation of data is that it can provide a dimensionality reduction of the data to the number of nodes plus edges in the graph. Graphs are useful discrete repre sentations of data that have already been used successfully to incorporate structural information in data to aid in semi-supervised learning techniques. In this thesis, an online lear
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Saldanha, Richard A. "Graph-theoretic methods in discrimination and classification." Thesis, University of Oxford, 1998. https://ora.ox.ac.uk/objects/uuid:3a06dee1-00e9-4b56-be8e-e991a570ced6.

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This thesis is concerned with the graphical modelling of multivariate data. The main aim of graphical modelling is to provide an easy to understand visual representation of, often complex, data relationships by fitting graphs to data. The graphs consist of nodes denoting random variables and connecting lines or edges are used to depict variable dependencies. Equivalently, the absence of particular edges in a graph describe conditional independencies between random variables. The resulting structure is called a conditional independence graph. The use of conditional independence graphs as a guid
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Ketkar, Nikhil S. "Empirical comparison of graph classification and regression algorithms." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Dissertations/Spring2009/n_ketkar_042409.pdf.

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Thesis (Ph. D.)--Washington State University, May 2009.<br>Title from PDF title page (viewed on June 3, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 101-108).
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Ferrer, Sumsi Miquel. "Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5788.

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Donat un conjunt d'objectes, el concepte genèric de mediana està de&#64257;nit com l'objecte amb la suma de distàncies a tot el conjunt, més petita. Sovint, aquest concepte és usat per a obtenir el representant del conjunt. <br/>En el reconeixement estructural de patrons, els grafs han estat usats normalment per a representar objectes complexos. En el domini dels grafs, el concepte de mediana és conegut com median graph. Potencialment, té les mateixes aplicacions que el concepte de mediana per poder ser usat com a representant d'un conjunt de grafs. <br/>Tot i la seva simple de&#64257;nició i
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Childs, Liam, Zoran Nikoloski, Patrick May, and Dirk Walther. "Identification and classification of ncRNA molecules using graph properties." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2010/4519/.

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The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functiona
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Ersahin, Kaan. "Segmentation and classification of polarimetric SAR data using spectral graph partitioning." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/14607.

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Polarimetric Synthetic Aperture Radar (POLSAR) data have been commercially available for the last few years, which has increased demand for its operational use in remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. Existing strategies for automated POLSAR data analysis have utilized the polarimetric attributes of pixels, which involve target decompositions based on physical, mathematical or statistical models. A well-established and widely-used technique is the Wisha
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Lee, Zed Heeje. "A graph representation of event intervals for efficient clustering and classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281947.

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Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this thesis, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix
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Wu, Jindong. "Pooling strategies for graph convolution neural networks and their effect on classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288953.

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With the development of graph neural networks, this novel neural network has been applied in a broader and broader range of fields. One of the thorny problems researchers face in this field is selecting suitable pooling methods for a specific research task from various existing pooling methods. In this work, based on the existing mainstream graph pooling methods, we develop a benchmark neural network framework that can be used to compare these different graph pooling methods. By using the framework, we compare four mainstream graph pooling methods and explore their characteristics. Furthermore
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Chandra, Nagasai. "Node Classification on Relational Graphs using Deep-RGCNs." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2265.

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Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data cha
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Lamont, Morné Michael Connell. "Binary classification trees : a comparison with popular classification methods in statistics using different software." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52718.

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Thesis (MComm) -- Stellenbosch University, 2002.<br>ENGLISH ABSTRACT: Consider a data set with a categorical response variable and a set of explanatory variables. The response variable can have two or more categories and the explanatory variables can be numerical or categorical. This is a typical setup for a classification analysis, where we want to model the response based on the explanatory variables. Traditional statistical methods have been developed under certain assumptions such as: the explanatory variables are numeric only and! or the data follow a multivariate normal distributio
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Books on the topic "Graph classification"

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Classification and regression trees. Chapman & Hall, 1993.

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Riesen, Kaspar. Graph classification and clustering based on vector space embedding. World Scientific, 2010.

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The classification of minimal graphs with given abelian automorphism group. American Mathematical Society, 1985.

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Cherlin, Gregory L. The classification of countable homogeneous directed graphs and countable homogeneous n-tournaments. American Mathematical Society, 1998.

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Argyros, S. A classification of separable Rosenthal compacta and its applications. Institute of Mathematics, Polish Academy of Sciences, 2008.

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Argyros, S. A classification of separable Rosenthal compacta and its applications. Institute of Mathematics, Polish Academy of Sciences, 2008.

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Hage, Per. Island networks: Communication, kinship, and classification structures in Oceania. Cambridge University Press, 1996.

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Ocneanu, Adrian. Quantum symmetry, differential geometry of finite graphs and classification of subfactors. Dept. of Mathematics, University of Tokyo, 1991.

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On the classification of C*-algebras of real rank zero: Inductive limits of matrix algebras over non-Hausdorff graphs. American Mathematical Society, 1995.

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Su, Hongbing. On the classification of C*, algebras of real rank zero: Inductive limits of matrix algebras over non-Hausdorff graphs. [s.n.], 1992.

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Book chapters on the topic "Graph classification"

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Tsuda, Koji, and Hiroto Saigo. "Graph Classification." In Managing and Mining Graph Data. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6045-0_11.

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Morris, Christopher. "Graph Neural Networks: Graph Classification." In Graph Neural Networks: Foundations, Frontiers, and Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6054-2_9.

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Guo, Ting, and Xingquan Zhu. "Super-Graph Classification." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06608-0_27.

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Auer, Christopher, Christian Bachmaier, Franz Josef Brandenburg, and Andreas Gleißner. "Classification of Planar Upward Embedding." In Graph Drawing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25878-7_39.

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Kataoka, Tetsuya, Eimi Shiotsuki, and Akihiro Inokuchi. "Graph Classification with Mapping Distance Graph Kernels." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93647-5_2.

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Vinh, Nguyen Duy, Akihiro Inokuchi, and Takashi Washio. "Graph Classification Based on Optimizing Graph Spectra." In Discovery Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16184-1_15.

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Qiu, Kunfeng, Jinchao Zhou, Hui Cui, Zhuangzhi Chen, Shilian Zheng, and Qi Xuan. "Time Series Classification Based on Complex Network." In Graph Data Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2609-8_10.

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Bonnington, C. Paul, and Charles H. C. Little. "Classification of Surfaces." In The Foundations of Topological Graph Theory. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2540-9_3.

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Adaloglou, Nikolas, Nicholas Vretos, and Petros Daras. "Multi-view Adaptive Graph Convolutions for Graph Classification." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58574-7_24.

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Le, Tuan, Marco Bertolini, Frank Noé, and Djork-Arné Clevert. "Parameterized Hypercomplex Graph Neural Networks for Graph Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86365-4_17.

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Conference papers on the topic "Graph classification"

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Zhu, Yuanyuan, Jeffrey Xu Yu, Hong Cheng, and Lu Qin. "Graph classification." In the 21st ACM international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2396791.

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Chen, Siheng, Aliaksei Sandryhaila, Jose M. F. Moura, and Jelena Kovacevic. "Adaptive graph filtering: Multiresolution classification on graphs." In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2013. http://dx.doi.org/10.1109/globalsip.2013.6736906.

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Nikolentzos, Giannis, Polykarpos Meladianos, Stratis Limnios, and Michalis Vazirgiannis. "A Degeneracy Framework for Graph Similarity." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/360.

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The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Most existing methods for graph similarity focus either on local or on global properties of graphs. However, even if graphs seem very similar from a local or a global perspective, they may exhibit different structure at different scales. In this paper, we present a general framework for graph similarity which takes into account structure at multiple different scales. The proposed framework capitalizes on the well-known k-core decomposition of graphs in order to bui
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Barros, Claudio D. T., Daniel N. R. da Silva, and Fabio A. M. Porto. "Machine Learning on Graph-Structured Data." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18179.

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Several real-world complex systems have graph-structured data, including social networks, biological networks, and knowledge graphs. A continuous increase in the quantity and quality of these graphs demands learning models to unlock the potential of this data and execute tasks, including node classification, graph classification, and link prediction. This tutorial presents machine learning on graphs, focusing on how representation learning - from traditional approaches (e.g., matrix factorization and random walks) to deep neural architectures - fosters carrying out those tasks. We also introdu
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Zhou, Kaixiong, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. "Multi-Channel Graph Neural Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/188.

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The classification of graph-structured data has be-come increasingly crucial in many disciplines. It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification applications. A straightforward way to leverage the hierarchical structure is to make use the pooling algorithms to cluster nodes into fixed groups, and shrink the input graph layer by layer to learn the pooled graphs.However, the pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the f
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Bai, Yunsheng, Hao Ding, Yang Qiao, et al. "Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/275.

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We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGraphEmb, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Sca
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Li, Pengyong, Jun Wang, Ziliang Li, et al. "Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/371.

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Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demon
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Shirui Pan, Xingquan Zhu, Chengqi Zhang, and P. S. Yu. "Graph stream classification using labeled and unlabeled graphs." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544842.

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Thanh Thi Ho, Tuyen, Hung Vu, and Bac Le. "Efficient Graph Classification via Graph Encoding Networks." In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2020. http://dx.doi.org/10.1109/rivf48685.2020.9140729.

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Wu, Jia, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, and Chengqi Zhang. "Multi-graph-view Learning for Graph Classification." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.97.

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Reports on the topic "Graph classification"

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Bagherjeiran, A., and C. Kamath. Graph-based Techniques for Orbit Classification: Early Results. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/885147.

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Passariello, Fausto. The Graph Classification for the venous system of the lower limb. Fondazione Vasculab, 2009. http://dx.doi.org/10.24019/2009.uip50graph.

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Merkurjev, Ekaterina, Egil Bae, Andrea L. Bertozzi, and Xue-Cheng Tai. Global Binary Optimization on Graphs for Classification of High Dimensional Data. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610270.

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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel se
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