Relevant bibliographies by topics / Random Graph

Contents

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

Academic literature on the topic 'Random Graph'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Random Graph"

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Shi, Haizhong, and Yue Shi. "Random graph languages." Discrete Mathematics, Algorithms and Applications 09, no. 02 (April 2017): 1750020. http://dx.doi.org/10.1142/s1793830917500203.

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There tend to be no related researches regarding the relationships between graph theory and languages ever since the concept of graph-semigroup was first proposed in 1991. In 2011, after finding out the inner co-relations among digraphs, undirected graphs and languages, we proposed certain concepts including undirected graph language and digraph language; moreover, in 2014, we proposed a broaden concept–(V,R)-language and proved: (1) both undirected graph language and digraph language are (V,R)-languages; (2) both undirected graph language and digraph language are regular languages; (3) natural languages are regular languages. In this paper, we propose a new concept–Random Graph Language and build the relationships between random graph and language, which provides researchers with the possibility to do research about languages by using random graph theory.
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Kim, J. H., and V. H. Vu. "Sandwiching random graphs: universality between random graph models." Advances in Mathematics 188, no. 2 (November 2004): 444–69. http://dx.doi.org/10.1016/j.aim.2003.10.007.

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FÜRER, MARTIN, and SHIVA PRASAD KASIVISWANATHAN. "Approximately Counting Embeddings into Random Graphs." Combinatorics, Probability and Computing 23, no. 6 (July 9, 2014): 1028–56. http://dx.doi.org/10.1017/s0963548314000339.

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LetHbe a graph, and letCH(G) be the number of (subgraph isomorphic) copies ofHcontained in a graphG. We investigate the fundamental problem of estimatingCH(G). Previous results cover only a few specific instances of this general problem, for example the case whenHhas degree at most one (the monomer-dimer problem). In this paper we present the first general subcase of the subgraph isomorphism counting problem, which is almost always efficiently approximable. The results rely on a new graph decomposition technique. Informally, the decomposition is a labelling of the vertices such that every edge is between vertices with different labels, and for every vertex all neighbours with a higher label have identical labels. The labelling implicitly generates a sequence of bipartite graphs, which permits us to break the problem of counting embeddings of large subgraphs into that of counting embeddings of small subgraphs. Using this method, we present a simple randomized algorithm for the counting problem. For all decomposable graphsHand all graphsG, the algorithm is an unbiased estimator. Furthermore, for all graphsHhaving a decomposition where each of the bipartite graphs generated is small and almost all graphsG, the algorithm is a fully polynomial randomized approximation scheme.We show that the graph classes ofHfor which we obtain a fully polynomial randomized approximation scheme for almost allGincludes graphs of degree at most two, bounded-degree forests, bounded-width grid graphs, subdivision of bounded-degree graphs, and major subclasses of outerplanar graphs, series-parallel graphs and planar graphs of large girth, whereas unbounded-width grid graphs are excluded. Moreover, our general technique can easily be applied to proving many more similar results.
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Chen, Lin, Li Zeng, Jin Peng, Junren Ming, and Xianghui Zhu. "Regularity Index of Uncertain Random Graph." Symmetry 15, no. 1 (January 3, 2023): 137. http://dx.doi.org/10.3390/sym15010137.

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A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.
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Janson, Svante. "Quasi-random graphs and graph limits." European Journal of Combinatorics 32, no. 7 (October 2011): 1054–83. http://dx.doi.org/10.1016/j.ejc.2011.03.011.

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Isufi, Elvin, Andreas Loukas, Andrea Simonetto, and Geert Leus. "Filtering Random Graph Processes Over Random Time-Varying Graphs." IEEE Transactions on Signal Processing 65, no. 16 (August 15, 2017): 4406–21. http://dx.doi.org/10.1109/tsp.2017.2706186.

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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 graph satisfying certain natural expansion properties is(n,Δ)-universal. Our methods also apply to the case when Δ is some function ofn. Since random graphs are known to be good expanders, our result implies, in particular, that there exists a positive constantcsuch that the random graphG(n,cn−1/3log2n) is asymptotically almost surely (a.a.s.) universal for(n,O(1)). Moreover, a corresponding result holds for the random regular graph of degreecn2/3log2n. Another interesting consequence is the existence of locally sparsen-vertex(n,Δ)-universal graphs. For example, we show that one can (randomly) constructn-vertex(n,O(1))-universal graphs with clique number at most five. This complements the construction of Bhatt, Chung, Leighton and Rosenberg (1989), whose(n,Δ)-universal graphs with merelyO(n)edges contain large cliques of size Ω(Δ). Finally, we show that random graphs are robustly(n,Δ)-universal in the context of the Maker–Breaker tree-universality game.
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Borbély, József, and András Sárközy. "Quasi-Random Graphs, Pseudo-Random Graphs and Pseudorandom Binary Sequences, I. (Quasi-Random Graphs)." Uniform distribution theory 14, no. 2 (December 1, 2019): 103–26. http://dx.doi.org/10.2478/udt-2019-0017.

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AbstractIn the last decades many results have been proved on pseudo-randomness of binary sequences. In this series our goal is to show that using many of these results one can also construct large families of quasi-random, pseudo-random and strongly pseudo-random graphs. Indeed, it will be proved that if the first row of the adjacency matrix of a circulant graph forms a binary sequence which possesses certain pseudorandom properties (and there are many large families of binary sequences known with these properties), then the graph is quasi-random, pseudo-random or strongly pseudo-random, respectively. In particular, here in Part I we will construct large families of quasi-random graphs along these lines. (In Parts II and III we will present and study constructions for pseudo-random and strongly pseudo-random graphs, respectively.)
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Meyfroyt, Thomas M. M. "Degree-dependent threshold-based random sequential adsorption on random trees." Advances in Applied Probability 50, no. 01 (March 2018): 302–26. http://dx.doi.org/10.1017/apr.2018.14.

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Abstract We consider a special version of random sequential adsorption (RSA) with nearest-neighbor interaction on infinite tree graphs. In classical RSA, starting with a graph with initially inactive nodes, each of the nodes of the graph is inspected in a random order and is irreversibly activated if none of its nearest neighbors are active yet. We generalize this nearest-neighbor blocking effect to a degree-dependent threshold-based blocking effect. That is, each node of the graph is assumed to have its own degree-dependent threshold and if, upon inspection of a node, the number of active direct neighbors is less than that node's threshold, the node will become irreversibly active. We analyze the activation probability of nodes on an infinite tree graph, given the degree distribution of the tree and the degree-dependent thresholds. We also show how to calculate the correlation between the activity of nodes as a function of their distance. Finally, we propose an algorithm which can be used to solve the inverse problem of determining how to set the degree-dependent thresholds in infinite tree graphs in order to reach some desired activation probabilities.
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Tang, Y., and Q. L. Li. "Zero-One Law for Connectivity in Superposition of Random Key Graphs on Random Geometric Graphs." Discrete Dynamics in Nature and Society 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/982094.

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We study connectivity property in the superposition of random key graph on random geometric graph. For this class of random graphs, we establish a new version of a conjectured zero-one law for graph connectivity as the number of nodes becomes unboundedly large. The results reported here strengthen recent work by the Krishnan et al.
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Dissertations / Theses on the topic "Random 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 vertices. In Chapter 2 we analyse random planar graphs with minimum degree two and three. Using techniques from analytic combinatorics and the concepts of core and kernel of a graph, we obtain precise asymptotic estimates and analyse relevant parameters for random graphs, such as the number of edges or the size of the core, where we obtain Gaussian limit laws. More challenging is the extremal parameter equal to the size of the largest tree attached to the core. In this case we obtain a logarithmic estimate for the expected value together with a concentration result. In Chapter 3 we study the number of subgraphs isomorphic to a fixed graph in subcritical classes of graphs. We obtain Gaussian limit laws with linear expectation and variance when the fixed graph is 2-connected. The main tool is the analysis of infinite systems of equations by Drmota, Gittenberger and Morgenbesser, using the theory of compact operators. Computing the exact constants for the first estimates of the moments is in general out of reach. For the class of series-parallel graphs we are able to compute them in some particular interesting cases. In Chapter 4 we enumerate (arbitrary) graphs where the degree of every vertex belongs to a fixed subset of the natural numbers. In this case the associated generating functions are divergent and our analysis uses instead the so-called configuration model. We obtain precise asymptotic estimates for the number of graphs with given number of vertices and edges and subject to the degree restriction. Our results generalize widely previous special cases, such as d-regular graphs or graphs with minimum degree at least d.
En aquesta tesi utilitzem l'analítica combinatòria per treballar amb dos problemes relacionats: enumeració de grafs i grafs aleatoris de classes de grafs amb restriccions. En particular ens interessa esbossar un dibuix general de determinades famílies de grafs determinant, en primer lloc, quants grafs hi ha de cada mida possible (enumeració de grafs), i, en segon lloc, quin és el comportament típic d'un element de mida fixa triat a l'atzar uniformement, quan aquesta mida tendeix a infinit (grafs aleatoris). Els problemes en què treballem tracten amb grafs que satisfan condicions globals, com ara ésser planars, o bé tenir restriccions en el grau dels vèrtexs. En el Capítol 2 analitzem grafs planar aleatoris amb grau mínim dos i tres. Mitjançant tècniques de combinatòria analítica i els conceptes de nucli i kernel d'un graf, obtenim estimacions asimptòtiques precises i analitzem paràmetres rellevants de grafs aleatoris, com ara el nombre d'arestes o la mida del nucli, on obtenim lleis límit gaussianes. També treballem amb un paràmetre que suposa un repte més important: el paràmetre extremal que es correspon amb la mida de l'arbre més gran que penja del nucli. En aquest cas obtenim una estimació logarítmica per al seu valor esperat, juntament amb un resultat sobre la seva concentració. En el Capítol 3 estudiem el nombre de subgrafs isomorfs a un graf fix en classes de grafs subcrítiques. Quan el graf fix és biconnex, obtenim lleis límit gaussianes amb esperança i variància lineals. L'eina principal és l'anàlisi de sistemes infinits d'equacions donada per Drmota, Gittenberger i Morgenbesser, que utilitza la teoria d'operadors compactes. El càlcul de les constants exactes de la primera estimació dels moments en general es troba fora del nostre abast. Per a la classe de grafs sèrie-paral·lels podem calcular les constants en alguns casos particulars interessants. En el Capítol 4 enumerem grafs (arbitraris) el grau de cada vèrtex dels quals pertany a un subconjunt fix dels nombres naturals. En aquest cas les funcions generatrius associades són divergents i la nostra anàlisi utilitza l'anomenat model de configuració. El nostre resultat consisteix a obtenir estimacions asimptòtiques precises per al nombre de grafs amb un nombre de vèrtexs i arestes donat, amb la restricció dels graus. Aquest resultat generalitza àmpliament casos particulars existents, com ara grafs d-regulars, o grafs amb grau mínim com a mínim d.
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Seierstad, Taral Guldahl. "The phase transition in random graphs and random graph processes." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985760044.

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Makai, Tamas. "Random graph processes." Thesis, Royal Holloway, University of London, 2012. http://repository.royalholloway.ac.uk/items/b24b89af-3fc1-4d2f-a673-64483a3bc2f2/8/.

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This thesis deals with random graph processes. More precisely it deals with two random graph processes which create H -free graphs. The first of these processes is the random H-elimination process which starts from the complete graph and in every step removes an edge uniformly at random from the set of edges which are found in a copy of H. The second is the H-free random graph process which starts from the empty graph and in every step an edge chosen uniformly at random from the set of edges which when added to the graph would not create a copy of H is inserted. We consider these graph processes for several classes of graphs H, for example strictly two balanced graphs. The class of strictly two balanced graphs includes among others cycles and complete graphs. We analysed the H-elimination process, when H is strictly 2-balanced. For this class we show the typical number of edges found at the end of the process. We also consider the sub graphs created by the process and its independence number. We also managed to show the expected number of edges in the H -elimination pro- cess when H = Ki, the graph created from the complete graph on 4 vertices by removing an edge and when H = K34 where K34 is created from the complete bi- partite graph with 3 vertices in one partition and'4 vertices in the second partition, by removing an edge. In case of the H -free process we considered the case when H is the triangle and showed that the triangle-free random graph process only creates sparse subgraphs. Finally we have improved the lower bound on the length of the K34-free random graph process. '
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Kang, Mihyun. "Random planar structures and random graph processes." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985516585.

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Roberts, Ekaterina Sergeevna. "Tailored random graph ensembles." Thesis, King's College London (University of London), 2014. https://kclpure.kcl.ac.uk/portal/en/theses/tailored-random-graph-ensembles(daefc925-24a3-4f7f-8c79-21b9136c636b).html.

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Tailored graph ensembles are a developing bridge between statistical mechanics and biological networks. In this thesis, this concept is used to generate a suite of rigorous mathematical tools to quantify and compare the topology of cellular signalling networks. Earlier published results to quantify the entropy of constrained random graph ensembles are extended by looking at constraints relating to directed graphs, bipartite graphs, neighbourhood compositions and generalised degrees. To incorporate constraints relating to the average number of short loops, a number of innovative techniques are reviewed and extended, moving the analysis beyond the usual tree-like assumption. The generation of unbiased sample networks under some of these new constraints is studied. A series of illustrations of how these concepts may be applied to systems biology are developed. Topological observables are obtained from real biological networks and the entropy of the associated random graph ensemble is calculated. Certain studies on over-represented motifs are replicated and the influence of the choice of null model is considered. The correlation between the topological role of each protein and its lethality is studied in yeast. Throughout, this document aims to promote looking at a network as a realisation satisfying certain constraints rather than just as a list of nodes and edges. This may initially seem to be an abstract approach, but it is in fact a more natural viewpoint within which to consider many fundamental questions regarding the origin, function and design of observed real networks.
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Warnke, Lutz. "Random graph processes with dependencies." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:71b48e5f-a192-4684-a864-ea9059a25d74.

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Random graph processes are basic mathematical models for large-scale networks evolving over time. Their systematic study was pioneered by Erdös and Rényi around 1960, and one key feature of many 'classical' models is that the edges appear independently. While this makes them amenable to a rigorous analysis, it is desirable, both mathematically and in terms of applications, to understand more complicated situations. In this thesis the main goal is to improve our rigorous understanding of evolving random graphs with significant dependencies. The first model we consider is known as an Achlioptas process: in each step two random edges are chosen, and using a given rule only one of them is selected and added to the evolving graph. Since 2000 a large class of 'complex' rules has eluded a rigorous analysis, and it was widely believed that these could give rise to a striking and unusual phenomenon. Making this explicit, Achlioptas, D'Souza and Spencer conjectured in Science that one such rule yields a very abrupt (discontinuous) percolation phase transition. We disprove this, showing that the transition is in fact continuous for all Achlioptas process. In addition, we give the first rigorous analysis of the more 'complex' rules, proving that certain key statistics are tightly concentrated (i) in the subcritical evolution, and (ii) also later on if an associated system of differential equations has a unique solution. The second model we study is the H-free process, where random edges are added subject to the constraint that they do not complete a copy of some fixed graph H. The most important open question for such 'constrained' processes is due to Erdös, Suen and Winkler: in 1995 they asked what the typical final number of edges is. While Osthus and Taraz answered this in 2000 up to logarithmic factors for a large class of graphs H, more precise bounds are only known for a few special graphs. We close this gap for the cases where a cycle of fixed length is forbidden, determining the final number of edges up to constants. Our result not only establishes several conjectures, it is also the first which answers the more than 15-year old question of Erdös et. al. for a class of forbidden graphs H.
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Ross, Christopher Jon. "Properties of Random Threshold and Bipartite Graphs." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306296991.

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Weinstein, Lee. "Empirical study of graph properties with particular interest towards random graphs." Diss., Connect to the thesis, 2005. http://hdl.handle.net/10066/1485.

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Riordan, Oliver Maxim. "Subgraphs of the discrete torus, random graphs and general graph invariants." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624757.

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Cooper, Jeffrey R. "Product Dimension of a Random Graph." Miami University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=miami1272038833.

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Books on the topic "Random Graph"

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Random graphs. London: Academic Press, 1985.

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

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Random graphs. 2nd ed. Cambridge: Cambridge University Press, 2001.

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Random geometric graphs. Oxford: Oxford University Press, 2003.

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Random graph dynamics. Cambridge: Cambridge University Press, 2007.

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

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O'Connell, Neil. Some large deviation results for sparse random graphs. Bristol [England]: Hewlett Packard, 1996.

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Orthogonal decompositions and functional limit theorems for random graph statistics. Providence, RI: American Mathematical Society, 1994.

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1949-, Rödl Vojtěch, Ruciński Andrzej, and Tetali Prasad, eds. A Sharp threshold for random graphs with a monochromatic triangle in every edge coloring. Providence, R.I: American Mathematical Society, 2006.

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Transfiniteness for graphs, electrical networks, and random walks. Boston: Birkhäuser, 1996.

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Book chapters on the topic "Random Graph"

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Diestel, Reinhard. "Random Graphs." In Graph Theory, 323–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53622-3_11.

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Diestel, Reinhard. "Random Graphs." In Graph Theory, 309–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-642-14279-6_11.

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Li, Yusheng, and Qizhong Lin. "Random Graph." In Applied Mathematical Sciences, 75–110. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12762-5_4.

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Bollobás, Béla. "Random Graphs." In Modern Graph Theory, 215–52. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0619-4_7.

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Zhang, Li-Chun. "Targeted random walk sampling." In Graph Sampling, 93–112. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003203490-6.

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Hurd, T. R. "Random Graph Models." In SpringerBriefs in Quantitative Finance, 45–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33930-6_3.

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Cameron, Peter J. "The Random Graph." In The Mathematics of Paul Erdős II, 353–78. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7254-4_22.

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Kamiński, Bogumił, Paweł Prałat, and François Théberge. "Random Graph Models." In Mining Complex Networks, 27–56. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003218869-2.

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Cameron, Peter J. "The Random Graph." In Algorithms and Combinatorics, 333–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60406-5_32.

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Raj P. M., Krishna, Ankith Mohan, and K. G. Srinivasa. "Random Graph Models." In Computer Communications and Networks, 45–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96746-2_3.

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Conference papers on the topic "Random Graph"

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Jin, Di, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, and Weixiong Zhang. "RAW-GNN: RAndom Walk Aggregation based Graph Neural Network." 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.2022/293.

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Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent. Several methods have attempted to address this heterophily problem, but they do not change the fundamental aggregation mechanism of GCNs because they rely on summation operators to aggregate information from neighboring nodes, which is implicitly subject to the homophily assumption. Here, we introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method. The proposed approach integrates the random walk strategy with graph neural networks. The new method utilizes breadth-first random walk search to capture homophily information and depth-first search to collect heterophily information. It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks. These designs make RAW-GNN suitable for both homophily and heterophily graphs. Extensive experimental results showed that the new method achieved state-of-the-art performance on a variety of homophily and heterophily graphs.
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Zhao, Xiangyu, Hanzhou Wu, and Xinpeng Zhang. "Watermarking Graph Neural Networks by Random Graphs." In 2021 9th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2021. http://dx.doi.org/10.1109/isdfs52919.2021.9486352.

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Aiello, William, Fan Chung, and Linyuan Lu. "A random graph model for massive graphs." In the thirty-second annual ACM symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/335305.335326.

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Feng, Lijin, and Jackson Barr. "Complete Graphs and Bipartite Graphs in a Random Graph." In 2021 5th International Conference on Vision, Image and Signal Processing (ICVISP). IEEE, 2021. http://dx.doi.org/10.1109/icvisp54630.2021.00054.

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Stanton, Isabelle. "Streaming Balanced Graph Partitioning Algorithms for Random Graphs." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.95.

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Nobari, Sadegh, Xuesong Lu, Panagiotis Karras, and Stéphane Bressan. "Fast random graph generation." In the 14th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1951365.1951406.

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Gama, Fernando, Elvin Isufi, Geert Leus, and Alejandro Ribeiro. "Control of Graph Signals Over Random Time-Varying Graphs." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462381.

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Saad, Leila Ben, Elvin Isufi, and Baltasar Beferull-Lozano. "Graph Filtering with Quantization over Random Time-varying Graphs." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969270.

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Karunaratne, Thashmee, and Henrik Boström. "Graph Propositionalization for Random Forests." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.113.

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Kang, U., Hanghang Tong, and Jimeng Sun. "Fast Random Walk Graph Kernel." In Proceedings of the 2012 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611972825.71.

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Reports on the topic "Random Graph"

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Chandrasekhar, Arun, and Matthew Jackson. Tractable and Consistent Random Graph Models. Cambridge, MA: National Bureau of Economic Research, July 2014. http://dx.doi.org/10.3386/w20276.

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Carley, Kathleen M., and Eunice J. Kim. Random Graph Standard Network Metrics Distributions in ORA. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada487516.

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Mesbahi, Mehran. Dynamic Security and Robustness of Networked Systems: Random Graphs, Algebraic Graph Theory, and Control over Networks. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada567125.

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McCulloh, Ian, Joshua Lospinoso, and Kathleen M. Carley. The Link Probability Model: A Network Simulation Alternative to the Exponential Random Graph Model. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada537329.

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Doerschuk, Peter C. University LDRD student progress report on descriptions and comparisons of brain microvasculature via random graph models. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1055646.

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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 18. Hilbert Press, 2018. http://dx.doi.org/10.56441/hilbertpress.1818.9585.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 20. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2048.3738.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 19. Hilbert Press, 2019. http://dx.doi.org/10.56441/hilbertpress.1927.9364.

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CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Schulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.

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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.
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Pawagi, Shaunak, and I. V. Ramakrishnan. Updating Properties of Directed Acyclic Graphs on a Parallel Random Access Machine. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada162954.

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