Log in

Relevant bibliographies by topics / Graph classification / Journal articles

To see the other types of publications on this topic, follow the link: Graph classification.

Journal articles on the topic 'Graph classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Graph classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Wang, Yunzhe, George Baciu, and Chenhui Li. "A Layout-Based Classification Method for Visualizing Time-Varying Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3441301.

Full text

Abstract:

Connectivity analysis between the components of large evolving systems can reveal significant patterns of interaction. The systems can be simulated by topological graph structures. However, such analysis becomes challenging on large and complex graphs. Tasks such as comparing, searching, and summarizing structures, are difficult due to the enormous number of calculations required. For time-varying graphs, the temporal dimension even intensifies the difficulty. In this article, we propose to reduce the complexity of analysis by focusing on subgraphs that are induced by closely related entities.

APA, Harvard, Vancouver, ISO, and other styles

12

Jiang, Qiangrong, and Jiajia Ma. "A novel graph kernel on chemical compound classification." Journal of Bioinformatics and Computational Biology 16, no. 06 (2018): 1850026. http://dx.doi.org/10.1142/s0219720018500269.

Full text

Abstract:

Considering the classification of compounds as a nonlinear problem, the use of kernel methods is a good choice. Graph kernels provide a nice framework combining machine learning methods with graph theory, whereas the essence of graph kernels is to compare the substructures of two graphs, how to extract the substructures is a question. In this paper, we propose a novel graph kernel based on matrix named the local block kernel, which can compare the similarity of partial substructures that contain any number of vertexes. The paper finally tests the efficacy of this novel graph kernel in comparis

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Yi, Lulu Wang, and Liandong Wang. "A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs." Entropy 20, no. 12 (2018): 984. http://dx.doi.org/10.3390/e20120984.

Full text

Abstract:

Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of r

APA, Harvard, Vancouver, ISO, and other styles

14

Madhawa, Kaushalya, and Tsuyoshi Murata. "Active Learning for Node Classification: An Evaluation." Entropy 22, no. 10 (2020): 1164. http://dx.doi.org/10.3390/e22101164.

Full text

Abstract:

Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being used as a solution to train classification models with less labeled instances by selecting only the most informative instances for labeling. This is especially important when the labeled data are scarce or the labeling process is expensive. In this paper, we study the application of active learning on attributed graphs. In this setting, the da

APA, Harvard, Vancouver, ISO, and other styles

15

Wang, Ying, Hongji Wang, Hui Jin, Xinrui Huang, and Xin Wang. "Exploring graph capsual network for graph classification." Information Sciences 581 (December 2021): 932–50. http://dx.doi.org/10.1016/j.ins.2021.10.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Qin, Jian, Li Liu, Hui Shen, and Dewen Hu. "Uniform Pooling for Graph Networks." Applied Sciences 10, no. 18 (2020): 6287. http://dx.doi.org/10.3390/app10186287.

Full text

Abstract:

The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate graph classification. Previous pooling methods were based on assigning a score to each node and then pooling only the highest-scoring nodes, which might throw away whole neighbourhoods of nodes and therefore information. Here, we proposed a novel pooling method UGPool with a new point-of-view on selecting nodes. UGPool learns node scores based on node features an

APA, Harvard, Vancouver, ISO, and other styles

17

GIBERT, JAUME, ERNEST VALVENY, and HORST BUNKE. "EMBEDDING OF GRAPHS WITH DISCRETE ATTRIBUTES VIA LABEL FREQUENCIES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 03 (2013): 1360002. http://dx.doi.org/10.1142/s0218001413600021.

Full text

Abstract:

Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different

APA, Harvard, Vancouver, ISO, and other styles

18

Zhao, Yifei, and Fengqin Yan. "Hyperspectral Image Classification Based on Sparse Superpixel Graph." Remote Sensing 13, no. 18 (2021): 3592. http://dx.doi.org/10.3390/rs13183592.

Full text

Abstract:

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based o

APA, Harvard, Vancouver, ISO, and other styles

19

Tavakkoli, Maryam, Arsham Borumand Saeid, and Nosratollah Shajareh Poursalavati. "Classification of posets using zero-divisor graphs." Mathematica Slovaca 68, no. 1 (2018): 21–32. http://dx.doi.org/10.1515/ms-2017-0076.

Full text

Abstract:

Abstract Halaš and Jukl associated the zero-divisor graph G to a poset (X,≤) with zero by declaring two distinct elements x and y of X to be adjacent if and only if there is no non-zero lower bound for {x, y}. We characterize all the graphs that can be realized as the zero-divisor graph of a poset. Using this, we classify posets whose zero-divisor graphs are the same. In particular we show that if V is an n-element set, then there exist $\begin{array}{} \sum\limits_{\log_2(n+1)\leq k\leq n}^{}\binom{n}{k}\binom{2^k-k-1}{n-k} \end{array} $ reduced zero-divisor graphs whose vertex sets are V.

APA, Harvard, Vancouver, ISO, and other styles

20

Perepelitsa, V. A., I. V. Kozin, and S. V. Kurapov. "Methods of classification and algorithms of graph coloring." Researches in Mathematics 16 (February 7, 2021): 135. http://dx.doi.org/10.15421/240816.

Full text

Abstract:

We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.

APA, Harvard, Vancouver, ISO, and other styles

21

Sevugapandi, N., and C. P. Chandran. "Classification Algorithm for Gene Expression Graph and Manhattan Distance." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 2 (2017): 472. http://dx.doi.org/10.11591/ijeecs.v5.i2.pp472-478.

Full text

Abstract:

This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for sampl

APA, Harvard, Vancouver, ISO, and other styles

22

Pogorelov, Boris A., and Marina A. Pudovkina. "Classification of distance-transitive orbital graphs of overgroups of the Jevons group." Discrete Mathematics and Applications 30, no. 1 (2020): 7–22. http://dx.doi.org/10.1515/dma-2020-0002.

Full text

Abstract:

AbstractThe Jevons group AS̃n is an isometry group of the Hamming metric on the n-dimensional vector space Vn over GF(2). It is generated by the group of all permutation (n × n)-matrices over GF(2) and the translation group on Vn. Earlier the authors of the present paper classified the submetrics of the Hamming metric on Vn for n ⩾ 4, and all overgroups of AS̃n which are isometry groups of these overmetrics. In turn, each overgroup of AS̃n is known to define orbital graphs whose “natural” metrics are submetrics of the Hamming metric. The authors also described all distance-transitive orbital g

APA, Harvard, Vancouver, ISO, and other styles

23

Peng, Hao, Jianxin Li, Qiran Gong, Yuanxin Ning, Senzhang Wang, and Lifang He. "Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5387–94. http://dx.doi.org/10.1609/aaai.v34i04.5987.

Full text

Abstract:

Graph classification is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel

APA, Harvard, Vancouver, ISO, and other styles

24

Hughes, Lloyd, Simon Streicher, Ekaterina Chuprikova, and Johan Du Preez. "A Cluster Graph Approach to Land Cover Classification Boosting." Data 4, no. 1 (2019): 10. http://dx.doi.org/10.3390/data4010010.

Full text

Abstract:

When it comes to land cover classification, the process of deriving the land classes is complex due to possible errors in algorithms, spatio-temporal heterogeneity of the Earth observation data, variation in availability and quality of reference data, or a combination of these. This article proposes a probabilistic graphical model approach, in the form of a cluster graph, to boost geospatial classifications and produce a more accurate and robust classification and uncertainty product. Cluster graphs can be characterized as a means of reasoning about geospatial data such as land cover classific

APA, Harvard, Vancouver, ISO, and other styles

25

Makarov, Ilya, Dmitrii Kiselev, Nikita Nikitinsky, and Lovro Subelj. "Survey on graph embeddings and their applications to machine learning problems on graphs." PeerJ Computer Science 7 (February 4, 2021): e357. http://dx.doi.org/10.7717/peerj-cs.357.

Full text

Abstract:

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. 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. Using the constructed feature spaces, many machine learning problems on graph

APA, Harvard, Vancouver, ISO, and other styles

26

Iordanskii, M. A. "A Constructive Classification of Graphs." Modeling and Analysis of Information Systems 19, no. 4 (2015): 144–53. http://dx.doi.org/10.18255/1818-1015-2012-4-144-153.

Full text

Abstract:

The classes of graphs closed regarding the set-theoretical operations of union and intersection are considered. Some constructive descriptions of the closed graph classes are set by the element and operational generating basses. Such bases have been constructed for many classes of graphs. The backward problems (when the generating bases are given and it is necessary to define the characteristic properties of corresponding graphs) are solved in the paper. Subsets of element and operational bases of the closed class of all graphs are considered as generating bases.

APA, Harvard, Vancouver, ISO, and other styles

27

Cho, Ilwoo, and Palle Jorgensen. "An Index for Graphs and Graph Groupoids." Axioms 11, no. 2 (2022): 47. http://dx.doi.org/10.3390/axioms11020047.

Full text

Abstract:

In this paper, we consider certain quantities that arise in the images of the so-called graph-tree indexes of graph groupoids. In text, the graph groupoids are induced by connected finite-directed graphs with more than one vertex. If a graph groupoid GG contains at least one loop-reduced finite path, then the order of G is infinity; hence, the canonical groupoid index G:K of the inclusion K⊆G is either ∞ or 1 (under the definition and a natural axiomatization) for the graph groupoids K of all “parts” K of G. A loop-reduced finite path generates a semicircular element in graph groupoid algebra.

APA, Harvard, Vancouver, ISO, and other styles

28

LING, BO, CI XUAN WU, and BEN GONG LOU. "PENTAVALENT SYMMETRIC GRAPHS OF ORDER." Bulletin of the Australian Mathematical Society 90, no. 3 (2014): 353–62. http://dx.doi.org/10.1017/s0004972714000616.

Full text

Abstract:

AbstractA complete classification is given of pentavalent symmetric graphs of order$30p$, where$p\ge 5$is a prime. It is proved that such a graph${\Gamma }$exists if and only if$p=13$and, up to isomorphism, there is only one such graph. Furthermore,${\Gamma }$is isomorphic to$\mathcal{C}_{390}$, a coset graph of PSL(2, 25) with${\sf Aut}\, {\Gamma }=\mbox{PSL(2, 25)}$, and${\Gamma }$is 2-regular. The classification involves a new 2-regular pentavalent graph construction with square-free order.

APA, Harvard, Vancouver, ISO, and other styles

29

Wu, Jia, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, and Chengqi Zhang. "Multi-graph-view subgraph mining for graph classification." Knowledge and Information Systems 48, no. 1 (2015): 29–54. http://dx.doi.org/10.1007/s10115-015-0872-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Ma, Tinghuai, Qian Pan, Hongmei Wang, Wenye Shao, Yuan Tian, and Najla Al-Nabhan. "Graph classification algorithm based on graph structure embedding." Expert Systems with Applications 161 (December 2020): 113715. http://dx.doi.org/10.1016/j.eswa.2020.113715.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Gogulamudi, Naga Chandrika, and E. SREENIVASA REDDY. "Graph Classification System using Normalized Graph Convolutional Networks." International Journal of System of Systems Engineering 11, no. 3/4 (2021): 1. http://dx.doi.org/10.1504/ijsse.2021.10041862.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Chandrika, G. Naga, and E. Srinivasa Reddy. "Graph classification system using normalised graph convolutional networks." International Journal of System of Systems Engineering 11, no. 3/4 (2021): 320. http://dx.doi.org/10.1504/ijsse.2021.121461.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Qiangrong, Jiang, and Qiu guang. "Graph kernels combined with the neural network on protein classification." Journal of Bioinformatics and Computational Biology 17, no. 05 (2019): 1950030. http://dx.doi.org/10.1142/s0219720019500306.

Full text

Abstract:

At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph kernel named vertex-edge similarity kernel (VES kernel) based on mixed matrix, the innovation point of which is taking the adjacency matrix of the graph as the sample vector of each vertex and calculating kernel values by finding the most similar vertex pair of two graphs. In addition, we combine the novel kernel with the neural network and the ex

APA, Harvard, Vancouver, ISO, and other styles

34

Ranjan, Ekagra, Soumya Sanyal, and Partha Talukdar. "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5470–77. http://dx.doi.org/10.1609/aaai.v34i04.5997.

Full text

Abstract:

Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse an

APA, Harvard, Vancouver, ISO, and other styles

35

Huang, Xiayuan, Xiangli Nie, and Hong Qiao. "PolSAR Image Feature Extraction via Co-Regularized Graph Embedding." Remote Sensing 12, no. 11 (2020): 1738. http://dx.doi.org/10.3390/rs12111738.

Full text

Abstract:

Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information. Moreover, the similarity measurement is a crucial factor for constructing a weighted graph. Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. For obtaining a satisfactory PolSAR image classification perfo

APA, Harvard, Vancouver, ISO, and other styles

36

Nikitin, Filipp, Olexandr Isayev, and Vadim Strijov. "DRACON: disconnected graph neural network for atom mapping in chemical reactions." Physical Chemistry Chemical Physics 22, no. 45 (2020): 26478–86. http://dx.doi.org/10.1039/d0cp04748a.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Pan, Weisen, Jian Li, Lisa Gao, et al. "Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification." Scientific Programming 2022 (January 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/6737080.

Full text

Abstract:

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high acc

APA, Harvard, Vancouver, ISO, and other styles

38

Jawhari, El Moustafa, Maurice Pouzet, and Ivan Rival. "A Classification of Reflexive Graphs: The use of “Holes”." Canadian Journal of Mathematics 38, no. 6 (1986): 1299–328. http://dx.doi.org/10.4153/cjm-1986-066-9.

Full text

Abstract:

The purpose of this article is to develop aspects of a classification theory for reflexive graphs. A first important step was already taken in [2]; throughout we follow, at least the spirit, of the classification theory for ordered sets initiated in [1].For a graph G let V(G) denote its vertex set and E(G) ⊆ V(G) × V(G) its edge set. A graph K is a subgraph of G if V(K) ⊆ V(G) and for a, b ∊ V(K), (a, b) ∊ E(K) just if (a, b) ∊ E(G). The subgraph K of G is a retract of G, and we write K ◅ G, if there is an edge-preserving map g of V(G) to V(K) satisfying g(v) = v for each v ∊ V(K); g is called

APA, Harvard, Vancouver, ISO, and other styles

39

Power, Stephen C., Igor A. Baburin, and Davide M. Proserpio. "Isotopy classes for 3-periodic net embeddings." Acta Crystallographica Section A Foundations and Advances 76, no. 3 (2020): 275–301. http://dx.doi.org/10.1107/s2053273320000625.

Full text

Abstract:

Entangled embedded periodic nets and crystal frameworks are defined, along with their dimension type, homogeneity type, adjacency depth and periodic isotopy type. Periodic isotopy classifications are obtained for various families of embedded nets with small quotient graphs. The 25 periodic isotopy classes of depth-1 embedded nets with a single-vertex quotient graph are enumerated. Additionally, a classification is given of embeddings of n-fold copies of pcu with all connected components in a parallel orientation and n vertices in a repeat unit, as well as demonstrations of their maximal symmet

APA, Harvard, Vancouver, ISO, and other styles

40

Tsiovkina, Ludmila Yu. "ON A CLASS OF EDGE-TRANSITIVE DISTANCE-REGULAR ANTIPODAL COVERS OF COMPLETE GRAPHS." Ural Mathematical Journal 7, no. 2 (2021): 136. http://dx.doi.org/10.15826/umj.2021.2.010.

Full text

Abstract:

The paper is devoted to the problem of classification of edge-transitive distance-regular antipodal covers of complete graphs. This extends the classification of those covers that are arc-transitive, which has been settled except for some tricky cases that remain to be considered, including the case of covers satisfying condition \(c_2=1\) (which means that every two vertices at distance 2 have exactly one common neighbour).Here it is shown that an edge-transitive distance-regular antipodal cover of a complete graph with \(c_2=1\) is either the second neighbourhood of a vertex in a Moore graph

APA, Harvard, Vancouver, ISO, and other styles

41

Yao, Huaxiu, Chuxu Zhang, Ying Wei, et al. "Graph Few-Shot Learning via Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6656–63. http://dx.doi.org/10.1609/aaai.v34i04.6142.

Full text

Abstract:

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxili

APA, Harvard, Vancouver, ISO, and other styles

42

Aalipour, G., S. Akbari, M. Behboodi, R. Nikandish, M. J. Nikmehr, and F. Shaveisi. "The Classification of the Annihilating-Ideal Graphs of Commutative Rings." Algebra Colloquium 21, no. 02 (2014): 249–56. http://dx.doi.org/10.1142/s1005386714000200.

Full text

Abstract:

Let R be a commutative ring and 𝔸(R) be the set of ideals with non-zero annihilators. The annihilating-ideal graph of R is defined as the graph 𝔸𝔾(R) with the vertex set 𝔸(R)* = 𝔸(R)\{(0)} and two distinct vertices I and J are adjacent if and only if IJ = (0). Here, we present some results on the clique number and the chromatic number of the annihilating-ideal graph of a commutative ring. It is shown that if R is an Artinian ring and ω (𝔸𝔾(R)) = 2, then R is Gorenstein. Also, we investigate commutative rings whose annihilating-ideal graphs are complete or bipartite.

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Chaozi, Jianli Wang, and Kainan Yao. "Global Random Graph Convolution Network for Hyperspectral Image Classification." Remote Sensing 13, no. 12 (2021): 2285. http://dx.doi.org/10.3390/rs13122285.

Full text

Abstract:

Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its limitations in modeling sample relations. Graph convolution network (GCN) has been introduced to HSI classification due to its demonstrated ability in processing sample relations. Introducing GCN into HSI classification, the key issue is how to transform HSI, a typical euclidean data, into non-euclidean data. To address this problem, we propose a s

APA, Harvard, Vancouver, ISO, and other styles

44

FANG, XIN GUI, JIE WANG, and SANMING ZHOU. "CLASSIFICATION OF TETRAVALENT -TRANSITIVE NONNORMAL CAYLEY GRAPHS OF FINITE SIMPLE GROUPS." Bulletin of the Australian Mathematical Society 104, no. 2 (2021): 263–71. http://dx.doi.org/10.1017/s0004972720001446.

Full text

Abstract:

AbstractA graph $\Gamma $ is called $(G, s)$ -arc-transitive if $G \le \text{Aut} (\Gamma )$ is transitive on the set of vertices of $\Gamma $ and the set of s-arcs of $\Gamma $ , where for an integer $s \ge 1$ an s-arc of $\Gamma $ is a sequence of $s+1$ vertices $(v_0,v_1,\ldots ,v_s)$ of $\Gamma $ such that $v_{i-1}$ and $v_i$ are adjacent for $1 \le i \le s$ and $v_{i-1}\ne v_{i+1}$ for $1 \le i \le s-1$ . A graph $\Gamma $ is called 2-transitive if it is $(\text{Aut} (\Gamma ), 2)$ -arc-transitive but not $(\text{Aut} (\Gamma ), 3)$ -arc-transitive. A Cayley graph $\Gamma $ of a group G i

APA, Harvard, Vancouver, ISO, and other styles

45

Jia Wu, Shirui Pan, Xingquan Zhu, and Zhihua Cai. "Boosting for Multi-Graph Classification." IEEE Transactions on Cybernetics 45, no. 3 (2015): 416–29. http://dx.doi.org/10.1109/tcyb.2014.2327111.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Zhan, Zikang. "Performance of Different Graph Neural Networks on Graph Classification." Journal of Physics: Conference Series 1607 (August 2020): 012091. http://dx.doi.org/10.1088/1742-6596/1607/1/012091.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Shirui Pan, Jia Wu, Xingquan Zhu, and Chengqi Zhang. "Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification." IEEE Transactions on Cybernetics 45, no. 5 (2015): 954–68. http://dx.doi.org/10.1109/tcyb.2014.2341031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Xiao, Fei, Yi Sun, Donggao Du, Xuelei Li, and Min Luo. "A Novel Malware Classification Method Based on Crucial Behavior." Mathematical Problems in Engineering 2020 (March 21, 2020): 1–12. http://dx.doi.org/10.1155/2020/6804290.

Full text

Abstract:

Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N-order subgraph (NSG) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and inform

APA, Harvard, Vancouver, ISO, and other styles

49

Ma, Tinghuai, Wenye Shao, Yongsheng Hao, and Jie Cao. "Graph classification based on graph set reconstruction and graph kernel feature reduction." Neurocomputing 296 (June 2018): 33–45. http://dx.doi.org/10.1016/j.neucom.2018.03.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Chen, Yunxiao, Xiaoou Li, Jingchen Liu, Gongjun Xu, and Zhiliang Ying. "Exploratory Item Classification Via Spectral Graph Clustering." Applied Psychological Measurement 41, no. 8 (2017): 579–99. http://dx.doi.org/10.1177/0146621617692977.

Full text

Abstract:

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster an

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!