Relevant bibliographies by topics / Graph

Contents

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

Academic literature on the topic 'Graph'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Graph"

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CSIKVÁRI, PÉTER, and ZOLTÁN LÓRÁNT NAGY. "The Density Turán Problem." Combinatorics, Probability and Computing 21, no. 4 (February 29, 2012): 531–53. http://dx.doi.org/10.1017/s0963548312000016.

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LetHbe a graph onnvertices and let the blow-up graphG[H] be defined as follows. We replace each vertexviofHby a clusterAiand connect some pairs of vertices ofAiandAjif (vi,vj) is an edge of the graphH. As usual, we define the edge density betweenAiandAjasWe study the following problem. Given densities γijfor each edge (i,j) ∈E(H), one has to decide whether there exists a blow-up graphG[H], with edge densities at least γij, such that one cannot choose a vertex from each cluster, so that the obtained graph is isomorphic toH,i.e., noHappears as a transversal inG[H]. We calldcrit(H) the maximal va
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Cappelletti, Luca, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr, Tiffany J. Callahan, Carlos Cano, Marcin P. Joachimiak, et al. "GRAPE for fast and scalable graph processing and random-walk-based embedding." Nature Computational Science 3, no. 6 (June 26, 2023): 552–68. http://dx.doi.org/10.1038/s43588-023-00465-8.

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AbstractGraph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based meth
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Liu, Yu, and Lihua You. "Further Results on the Nullity of Signed Graphs." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/483735.

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The nullity of a graph is the multiplicity of the eigenvalue zero in its spectrum. A signed graph is a graph with a sign attached to each of its edges. In this paper, we apply the coefficient theorem on the characteristic polynomial of a signed graph and give two formulae on the nullity of signed graphs with cut-points. As applications of the above results, we investigate the nullity of the bicyclic signed graphΓ∞p,q,l, obtain the nullity set of unbalanced bicyclic signed graphs, and thus determine the nullity set of bicyclic signed graphs.
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Ji, Shengwei, Chenyang Bu, Lei Li, and Xindong Wu. "Local Graph Edge Partitioning." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–25. http://dx.doi.org/10.1145/3466685.

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Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may resul
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Kok, Johan. "Note: Certain bounds in respect of upper deg-centric graphs." Open Journal of Discrete Applied Mathematics 7, no. 2 (December 31, 2024): 1–6. https://doi.org/10.30538/psrp-odam2024.0097.

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This note presents some upper bounds for the size of the upper deg-centric grapg \(G_{ud}\) of a simple connected graph G. Amongst others, a result for graphs for which a compliant graph \(G\) has \(G_{ud} \cong \overline G\) is presented. Finally, results for size minimality in respect upper deg-centrication and minimum size of such graph \(G\) are presented.
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Duan, Yucong, Lixu Shao, and Gongzhu Hu. "Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph." International Journal of Software Innovation 6, no. 2 (April 2018): 10–25. http://dx.doi.org/10.4018/ijsi.2018040102.

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Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledg
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Sohn, Moo Young, and Jaeun Lee. "Characteristic polynomials of some weighted graph bundles and its application to links." International Journal of Mathematics and Mathematical Sciences 17, no. 3 (1994): 503–10. http://dx.doi.org/10.1155/s0161171294000748.

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In this paper, we introduce weighted graph bundles and study their characteristic polynomial. In particular, we show that the characteristic polynomial of a weightedK2(K¯2)-bundles over a weighted graphG?can be expressed as a product of characteristic polynomials two weighted graphs whose underlying graphs areGAs an application, we compute the signature of a link whose corresponding weighted graph is a double covering of that of a given link.
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JOHANNSEN, DANIEL, MICHAEL KRIVELEVICH, and WOJCIECH SAMOTIJ. "Expanders Are Universal for the Class of All Spanning Trees." Combinatorics, Probability and Computing 22, no. 2 (January 3, 2013): 253–81. http://dx.doi.org/10.1017/s0963548312000533.

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A graph is calleduniversalfor a given graph class(or, equivalently,-universal) if it contains a copy of every graph inas a subgraph. The construction of sparse universal graphs for various classeshas received a considerable amount of attention. There is particular interest in tight-universal graphs, that is, graphs whose number of vertices is equal to the largest number of vertices in a graph from. Arguably, the most studied case is that whenis some class of trees. In this work, we are interested in(n,Δ), the class of alln-vertex trees with maximum degree at most Δ. We show that everyn-vertex
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Kaviya, S., G. Mahadevan, and C. Sivagnanam. "Generalizing TCCD-Number For Power Graph Of Some Graphs." Indian Journal Of Science And Technology 17, SPI1 (April 25, 2024): 115–23. http://dx.doi.org/10.17485/ijst/v17sp1.243.

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Objective: Finding the triple connected certified domination number for the power graph of some peculiar graphs. Methods: A dominating set with the condition that every vertex in has either zero or at least two neighbors in and is triple connected is a called triple connected certified domination number of a graph. The minimum cardinality among all the triple connected certified dominating sets is called the triple connected certified domination number and is denoted by . The upper bound and lower bound of for the given graphs is found and then proved the upper bound and lower bound of were eq
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Simonet, Geneviève, and Anne Berry. "Properties and Recognition of Atom Graphs." Algorithms 15, no. 8 (August 19, 2022): 294. http://dx.doi.org/10.3390/a15080294.

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The atom graph of a connected graph is a graph whose vertices are the atoms obtained by clique minimal separator decomposition of this graph, and whose edges are the edges of all its atom trees. A graph G is an atom graph if there is a graph whose atom graph is isomorphic to G. We study the class of atom graphs, which is also the class of atom graphs of chordal graphs, and the associated recognition problem. We prove that each atom graph is a perfect graph and give a characterization of atom graphs in terms of a spanning tree, inspired by the characterization of clique graphs of chordal graphs
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Dissertations / Theses on the topic "Graph"

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Ramos, Garrido Lander. "Graph enumeration and random graphs." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/405943.

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In this thesis we use analytic combinatorics to deal with two related problems: graph enumeration and random graphs from constrained classes of graphs. We are interested in drawing a general picture of some graph families by determining, first, how many elements are there of a given possible size (graph enumeration), and secondly, what is the typical behaviour of an element of fixed size chosen uniformly at random, when the size tends to infinity (random graphs). The problems concern graphs subject to global conditions, such as being planar and/or with restrictions on the degrees of the verti
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Xu, Jingbo. "GRAPE : parallel graph query engine." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28927.

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The need for graph computations is evident in a multitude of use cases. To support computations on large-scale graphs, several parallel systems have been developed. However, existing graph systems require users to recast algorithms into new models, which makes parallel graph computations as a privilege to experienced users only. Moreover, real world applications often require much more complex graph processing workflows than previously evaluated. In response to these challenges, the thesis presents GRAPE, a distributed graph computation system, shipped with various applications for social netw
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Hearon, Sean M. "PLANAR GRAPHS, BIPLANAR GRAPHS AND GRAPH THICKNESS." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/427.

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A graph is planar if it can be drawn on a piece of paper such that no two edges cross. The smallest complete and complete bipartite graphs that are not planar are K5 and K{3,3}. A biplanar graph is a graph whose edges can be colored using red and blue such that the red edges induce a planar subgraph and the blue edges induce a planar subgraph. In this thesis, we determine the smallest complete and complete bipartite graphs that are not biplanar.
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Zuffi, Lorenzo. "Simplicial Complexes From Graphs Toward Graph Persistence." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13519/.

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Persistent homology is a branch of computational topology which uses geometry and topology for shape description and analysis. This dissertation is an introductory study to link persistent homology and graph theory, the connection being represented by various methods to build simplicial complexes from a graph. The methods we consider are the complex of cliques, of independent sets, of neighbours, of enclaveless sets and complexes from acyclic subgraphs, each revealing several properties of the underlying graph. Moreover, we apply the core ideas of persistence theory in the new context of graph
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Dusart, Jérémie. "Graph searches with applications to cocomparability graphs." Paris 7, 2014. http://www.theses.fr/2014PA077048.

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Un parcours de graphe est un mécanisme pour visiter de manière itérative les sommets d'un graphe. Cela a été une technique fondamentale dans la conception des algorithmes de graphe depuis les débuts de l'informatique. Bon nombre des premiers parcours étaient basées sur le parcours en largeur(BFS) ou en profondeur (DFS) et cela a donné des algorithmes efficaces pour les problèmes pratiques tels que la distance entre deux sommets, le diamètre, la connectivité, les problèmes de flot et la reconnaissance des graphes planaires. Le but de cette thèse est d'étudier les parcours de graphe Dans cette t
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Myers, Joseph Samuel. "Extremal theory of graph minors and directed graphs." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619614.

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Henry, Tyson Rombauer. "Interactive graph layout: The exploration of large graphs." Diss., The University of Arizona, 1992. http://hdl.handle.net/10150/185833.

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Directed and undirected graphs provide a natural notation for describing many fundamental structures of computer science. Unfortunately graphs are hard to draw in an easy to read fashion. Traditional graph layout algorithms have focused on creating good layouts for the entire graph. This approach works well with smaller graphs, but often cannot produce readable layouts for large graphs. This dissertation presents a novel methodology for viewing large graphs. The basic concept is to allow the user to interactively navigate through large graphs, learning about them in appropriately small and con
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Araujo, Julio. "Graph coloring and graph convexity." Nice, 2012. http://www.theses.fr/2012NICE4032.

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Dans cette thèse, nous étudions plusieurs problèmes de théorie des graphes concernant la coloration et la convexité des graphes. La plupart des résultats figurant ici sont liés à la complexité de calcul de ces problèmes pour certaines classes de graphes. Dans la première, et principale, partie de cette thèse, nous traitons la coloration des graphes qui est l’un des domaines les plus étudiés de théorie des graphes. Nous considérons d’abord trois problèmes de coloration appelés coloration gloutonne, coloration pondérée et coloration pondérée impropre. Ensuite, nous traitons un problème de décisi
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Peternek, Fabian Hans Adolf. "Graph compression using graph grammars." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31094.

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This thesis presents work done on compressed graph representations via hyperedge replacement grammars. It comprises two main parts. Firstly the RePair compression scheme, known for strings and trees, is generalized to graphs using graph grammars. Given an object, the scheme produces a small context-free grammar generating the object (called a “straight-line grammar”). The theoretical foundations of this generalization are presented, followed by a description of a prototype implementation. This implementation is then evaluated on real-world and synthetic graphs. The experiments show that severa
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Winerip, Jason. "Graph Linear Complexity." Scholarship @ Claremont, 2008. https://scholarship.claremont.edu/hmc_theses/216.

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This thesis expands on the notion of linear complexity for a graph as defined by Michael Orrison and David Neel in their paper "The Linear Complexity of a Graph." It considers additional classes of graphs and provides upper bounds for additional types of graphs and graph operations.
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Books on the topic "Graph"

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Golumbic, Martin Charles. Algorithmic graph theory and perfect graphs. 2nd ed. Amsterdam: North Holland, 2004.

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Bonato, Anthony. The game of cops and robbers on graphs. Providence, R.I: American Mathematical Society, 2011.

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Kolchin, V. F. Random graphs. Cambridge, UK: Cambridge University Press, 1999.

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Golumbic, Martin Charles. Algorithmic graph theory and perfect graphs. Amsterdam: Elsevier, 2004.

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Evstigneev, V. A. Teorii͡a︡ grafov: Algoritmy obrabotki beskonturnykh grafov. Novosibirsk: "Nauka," Sibirskoe predprii͡a︡tie RAN, 1998.

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Reinschke, K. J. Multivariable control: A graph-theoretic approach. Berlin: Springer-Verlag, 1988.

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Reinschke, K. J. Multivariable Control: A graph-theoretic approach. Berlin: Akademie-Verlag, 1988.

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1949-, Hahn Geňa, and Sabidussi Gert, eds. Graph symmetry: Algebraic methods and applications. Dordrecht: Kluwer Academic Publishers, 1997.

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West, Douglas Brent. Introduction to graph theory. Upper Saddle River, NJ: Prentice Hall, 1996.

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Bader, David, Henning Meyerhenke, Peter Sanders, and Dorothea Wagner, eds. Graph Partitioning and Graph Clustering. Providence, Rhode Island: American Mathematical Society, 2013. http://dx.doi.org/10.1090/conm/588.

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Book chapters on the topic "Graph"

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Matsui, Yasuko, and Shin-Ichi Nakano. "Cost Graph Colorings." In Algorithmic Foundations for Social Advancement, 345–52. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0668-9_22.

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Abstract Graph colorings are ubiquitous in the modeling of real-world problems. There are many applications and conjectures, which are still open and studied by various mathematicians and computer scientists. In this paper, we deal with cost graph colorings as an important subfield of graph colorings. In cost graph coloring, each color has a distinct cost, and we need to pay the cost each time to color each vertex or edge. Our task is to find a coloring with the minimum total cost. The cost coloring problems are NP-hard in general; however, polynomial time algorithms are known for certain classes of graphs.
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Kimoto, Kazufumi. "Generalized Group–Subgroup Pair Graphs." In International Symposium on Mathematics, Quantum Theory, and Cryptography, 169–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_14.

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Abstract A regular finite graph is called a Ramanujan graph if its zeta function satisfies an analog of the Riemann Hypothesis. Such a graph has a small second eigenvalue so that it is used to construct cryptographic hash functions. Typically, explicit family of Ramanujan graphs are constructed by using Cayley graphs. In the paper, we introduce a generalization of Cayley graphs called generalized group–subgroup pair graphs, which are a generalization of group–subgroup pair graphs defined by Reyes-Bustos. We study basic properties, especially spectra of them.
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Corradini, Andrea, Barbara König, and Dennis Nolte. "Specifying Graph Languages with Type Graphs." In Graph Transformation, 73–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61470-0_5.

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Xu, Jin. "Graph Theory Fundamentals." In Maximal Planar Graph Theory and the Four-Color Conjecture, 1–35. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4745-3_1.

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Abstract In this section, we will discuss some of the basic terminologies and concepts of graph theory, which will be assumed throughout the rest of this book, together with a few fundamental properties that characterize planar graphs, e.g., the well-known Kuratowski Theorem and planarity testing algorithm, etc.
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Shekhar, Shashi, and Hui Xiong. "Graph." In Encyclopedia of GIS, 409. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_546.

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Hinterberger, Hans. "Graph." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_1374-2.

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Calì, Carmelo. "Graph." In Lecture Notes in Morphogenesis, 225–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51324-5_49.

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Weik, Martin H. "graph." In Computer Science and Communications Dictionary, 687. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8022.

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Ramon, Jan. "Graph." In Encyclopedia of Systems Biology, 853. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1289.

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Hinterberger, Hans. "Graph." In Encyclopedia of Database Systems, 1260–61. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1374.

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Conference papers on the topic "Graph"

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Jothi, R. Mary Jeya, D. Angel, and Emalda Roslin. "On the Graph Join of Two Graph Classes of SSP Graphs." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC), 1–3. IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808535.

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Zhang, Xiaotong, Han Liu, Qimai Li, and Xiao-Ming Wu. "Attributed Graph Clustering via Adaptive Graph Convolution." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/601.

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Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution o
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Pan, Shirui, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Adversarially Regularized Graph Autoencoder for Graph Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/362.

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Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representa
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Kurapov, Sergey Vsevolodovich, and Maxim Vladimirovich Davidovsky. "Diakoptics and structures of graph." In Academician O.B. Lupanov 14th International Scientific Seminar "Discrete Mathematics and Its Applications". Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/dms-2022-59.

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In this paper, we consider the issues of determining the isomorphism separable graphs. It is shown that for a graph of any kind it is possible apply the methods of diacoptics, that is, divide the set of vertices of the graph into two subsets. The first subset of vertices characterizes non-separable part of the graph and is a non-separable graph. The second subset characterizes the additional part of the graph and consists from certain parts of the graph. The first stage of checking isomorphism consists in checking the isomorphism of non-separable parts of graphs. Then additional parts of the g
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Lu, Zhiyuan, Yuan Fang, Cheng Yang, and Chuan Shi. "Heterogeneous Graph Transformer with Poly-Tokenization." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/247.

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Graph neural networks have shown widespread success for learning on graphs, but they still face fundamental drawbacks, such as limited expressive power, over-smoothing, and over-squashing. Meanwhile, the transformer architecture offers a potential solution to these issues. However, existing graph transformers primarily cater to homogeneous graphs and are unable to model the intricate semantics of heterogeneous graphs. Moreover, unlike small molecular graphs where the entire graph can be considered as the receptive field in graph transformers, real-world heterogeneous graphs comprise a signific
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Bai, Yunsheng, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang. "Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: 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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Hu, Minyang, Hong Chang, Bingpeng Ma, and Shiguang Shan. "Learning Continuous Graph Structure with Bilevel Programming for Graph Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/424.

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Learning graph structure for graph neural networks (GNNs) is crucial to facilitate the GNN-based downstream learning tasks. It is challenging due to the non-differentiable discrete graph structure and lack of ground-truth. In this paper, we address these problems and propose a novel graph structure learning framework for GNNs. Firstly, we directly model the continuous graph structure with dual-normalization, which implicitly imposes sparse constraint and reduces the influence of noisy edges. Secondly, we formulate the whole training process as a bilevel programming problem, where the inner obj
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Wang, Chenhao, Yong Liu, Yan Yang, and Wei Li. "HeterGCL: Graph Contrastive Learning Framework on Heterophilic Graph." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/265.

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Graph Contrastive Learning (GCL) has attracted significant research attention due to its self-supervised ability to learn robust node representations. Unfortunately, most methods primarily focus on homophilic graphs, rendering them less effective for heterophilic graphs. In addition, the complexity of node interactions in heterophilic graphs poses considerable challenges to augmentation schemes, coding architectures, and contrastive designs for traditional GCL. In this work, we propose HeterGCL, a novel graph contrastive learning framework with structural and semantic learning to explore the t
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Chen, Zijian, Rong-Hua Li, Hongchao Qin, Huanzhong Duan, Yanxiong Lu, Qiangqiang Dai, and Guoren Wang. "Filtration-Enhanced Graph Transformation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/276.

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Graph kernels and graph neural networks (GNNs) are widely used for the classification of graph data. However, many existing graph kernels and GNNs have limited expressive power, because they cannot distinguish graphs if the classic 1-dimensional Weisfeiler-Leman (1-WL) algorithm does not distinguish them. To break the 1-WL expressiveness barrier, we propose a novel method called filtration-enhanced graph transformation, which is based on a concept from the area of topological data analysis. In a nutshell, our approach first transforms each original graph into a filtration-enhanced graph based
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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}. California: 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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Reports on the topic "Graph"

1

Selleck, C. B. GRAPH III: a digitizing and graph plotting program. Office of Scientific and Technical Information (OSTI), March 1986. http://dx.doi.org/10.2172/5868900.

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Lothian, Joshua, Sarah S. Powers, Blair D. Sullivan, Matthew B. Baker, Jonathan Schrock, and Stephen W. Poole. Graph Generator Survey. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1122669.

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Burch, Kimberly Jordan. Chemical Graph Theory. Washington, DC: The MAA Mathematical Sciences Digital Library, August 2008. http://dx.doi.org/10.4169/loci002857.

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Maunz, Peter Lukas Wilhelm, Jonathan David Sterk, Daniel Lobser, Ojas D. Parekh, and Ciaran Ryan-Anderson. Quantum Graph Analysis. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1235806.

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Phillips, Cynthia A. Parallel Graph Contraction. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada211916.

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Rasmussen, Craig W. Conditional Graph Completions. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada282914.

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Chen, Yudong, Sujay Sanghavi, and Huan Xu. Improved graph clustering. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada596381.

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Parekh, Ojas, Yipu Wang, Yang Ho, Cynthia Phillips, Ali Pinar, James Aimone, and William Severa. Neuromorphic Graph Algorithms. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1829422.

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Goodman, Eric. Graph Offerings Evaluation. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1173145.

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Hrebeniuk, Bohdan V. Modification of the analytical gamma-algorithm for the flat layout of the graph. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2882.

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The planarity of graphs is one of the key sections of graph theory. Although a graph is an abstract mathematical object, most often it is graph visualization that makes it easier to study or develop in a particular area, for example, the infrastructure of a city, a company’s management or a website’s web page. In general, in the form of a graph, it is possible to depict any structures that have connections between the elements. But often such structures grow to such dimensions that it is difficult to determine whether it is possible to represent them on a plane without intersecting the bonds.
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