Relevant bibliographies by topics / Representations of graphs

Contents

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

Academic literature on the topic 'Representations of graphs'

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

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Journal articles on the topic "Representations of graphs"

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Ashrafi, Ali Reza, Alain Bretto, and Alain Faisant. "Linear Representation of Graphs: Applications to Molecular Graphs." Match - Communications in Mathematical and in Computer Chemistry 90, no. 1 (2023): 5–18. http://dx.doi.org/10.46793/match.90-1.005a.

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In this article we build a linear representation starting from a multigraph; this allows us to give an algebraic view of the multigraph we are studying. We show that two isomorphic multigraphs give equivalent representations; conversely two equivalent representations give isomorphic multigraphs. For the clarity of the article we give at the beginning, classical results on representations, nevertheless these are specific to our graph representation.
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LUO, BIN, EDWIN HANCOCK, and RICHARD WILSON. "EIGENSPACES FOR GRAPHS." International Journal of Image and Graphics 02, no. 02 (2002): 247–68. http://dx.doi.org/10.1142/s0219467802000603.

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In this paper, we investigate the feasibility of using graph-based descriptions to learn the view structure of 3D objects. The graphs used in our study are constructed from the Delaunay triangulations of corner features. The investigation is divided into two parts. We commence by considering how relational structures can be encoded in a way which can be used to generate parametric eigenspaces. Here we investigate four different relational representations derived from the graphs. The first three of these are vector encodings of the adjacency graph, the weighted adjacency graph, and the point pr
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McKee, Terry A. "Clique graph representations of ptolemaic graphs." Discussiones Mathematicae Graph Theory 30, no. 4 (2010): 651. http://dx.doi.org/10.7151/dmgt.1520.

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Sun, Li, Zhongbao Zhang, Jiawei Zhang, et al. "Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.

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Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representations in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TG
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Ghoniem, Mohammad, Jean-Daniel Fekete, and Philippe Castagliola. "On the Readability of Graphs Using Node-Link and Matrix-Based Representations: A Controlled Experiment and Statistical Analysis." Information Visualization 4, no. 2 (2005): 114–35. http://dx.doi.org/10.1057/palgrave.ivs.9500092.

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In this article, we describe a taxonomy of generic graph related tasks along with a computer-based evaluation designed to assess the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation encompasses seven generic tasks and leads to insightful recommendations for the representation of graphs according to their size and density. Typically, we show that when graphs are bigger than twenty vertices, the matrix-based visualization outperforms node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams
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Vaghani, Dev. "An Approch for Representation of Node Using Graph Transformer Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 27–37. http://dx.doi.org/10.22214/ijraset.2023.48485.

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Abstract: In representation learning on graphs, graph neural networks (GNNs) have been widely employed and have attained cutting-edge performance in tasks like node categorization and link prediction. However, the majority of GNNs now in use are made to learn node representations on homogenous and fixed graphs. The limits are particularly significant when learning representations on a network that has been incorrectly described or one that is heterogeneous, or made up of different kinds of nodes and edges. This study proposes Graph Transformer Networks (GTNs), which may generate new network st
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Keller, René, Claudia M. Eckert, and P. John Clarkson. "Matrices or Node-Link Diagrams: Which Visual Representation is Better for Visualising Connectivity Models?" Information Visualization 5, no. 1 (2006): 62–76. http://dx.doi.org/10.1057/palgrave.ivs.9500116.

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Adjacency matrices or DSMs (design structure matrices) and node-link diagrams are both visual representations of graphs, which are a common form of data in many disciplines. DSMs are used throughout the engineering community for various applications, such as process modelling or change prediction. However, outside this community, DSMs (and other matrix-based representations of graphs) are rarely applied and node-link diagrams are very popular. This paper will examine, which representation is more suitable for visualising graphs. For this purpose, several user experiments were conducted that ai
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RIESEN, KASPAR, and HORST BUNKE. "GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1053–81. http://dx.doi.org/10.1142/s021800140900748x.

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Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propos
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FARSI, CARLA, ELIZABETH GILLASPY, PALLE JORGENSEN, SOORAN KANG, and JUDITH PACKER. "Monic representations of finite higher-rank graphs." Ergodic Theory and Dynamical Systems 40, no. 5 (2018): 1238–67. http://dx.doi.org/10.1017/etds.2018.79.

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In this paper, we define the notion of monic representation for the$C^{\ast }$-algebras of finite higher-rank graphs with no sources, and we undertake a comprehensive study of them. Monic representations are the representations that, when restricted to the commutative$C^{\ast }$-algebra of the continuous functions on the infinite path space, admit a cyclic vector. We link monic representations to the$\unicode[STIX]{x1D6EC}$-semibranching representations previously studied by Farsi, Gillaspy, Kang and Packer (Separable representations, KMS states, and wavelets for higher-rank graphs.J. Math. An
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Wang, Ruize, Zhongyu Wei, Piji Li, Qi Zhang, and Xuanjing Huang. "Storytelling from an Image Stream Using Scene Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9185–92. http://dx.doi.org/10.1609/aaai.v34i05.6455.

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Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult to interpret. We argue that translating each image into a graph-based semantic representation, i.e., scene graph, which explicitly encodes the objects and relationships detected within image, would benefit representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on
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Dissertations / Theses on the topic "Representations of graphs"

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Faubert, Glenn E. "Caterpillar tolerance representations of graphs /." View online ; access limited to URI, 2005. http://0-wwwlib.umi.com.helin.uri.edu/dissertations/dlnow/3186904.

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Rosar, Kós Lassance Carlos Eduardo. "Graphs for deep learning representations." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.

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Ces dernières années, les méthodes d'apprentissage profond ont atteint l'état de l'art dans une vaste gamme de tâches d'apprentissage automatique, y compris la classification d'images et la traduction automatique. Ces architectures sont assemblées pour résoudre des tâches d'apprentissage automatique de bout en bout. Afin d'atteindre des performances de haut niveau, ces architectures nécessitent souvent d'un très grand nombre de paramètres. Les conséquences indésirables sont multiples, et pour y remédier, il est souhaitable de pouvoir comprendre ce qui se passe à l'intérieur des architectures d
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Light, J. Bowman. "Intersections and representations of graphs." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1246559589/.

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Minton, Gregory. "Dot Product Representations of Graphs." Scholarship @ Claremont, 2008. https://scholarship.claremont.edu/hmc_theses/209.

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We introduce the concept of dot product representations of graphs, giving some motivations as well as surveying the previously known results. We extend these representations to more general fields, looking at the complex numbers, rational numbers, and finite fields. Finally, we study the behavior of dot product representations in field extensions.
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Trinks, Martin. "Graph polynomials and their representations." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2012. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-94991.

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Graph polynomials are polynomials associated to graphs that encode the number of subgraphs with given properties. We list different frameworks used to define graph polynomials in the literature. We present the edge elimination polynomial and introduce several graph polynomials equivalent to it. Thereby, we connect a recursive definition to the counting of colorings and to the counting of (spanning) subgraphs. Furthermore, we define a graph polynomial that not only generalizes the mentioned, but also many of the well-known graph polynomials, including the Potts model, the matching polynomial, t
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Cowen, Lenore Jennifer. "On local representations of graphs and networks." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12326.

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Knezevic, Marica. "Graphs of lattices in representations of finite groups." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/graphs-of-lattices-in-representations-of-finite-groups(2f435ed5-4a11-4d45-ae2b-26e3c6264ee8).html.

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This thesis work is motivated by the Langlands program, which relates objects from number theory and representation theory. In particular it is motivated by the compatibility of the local and global cases, especially in the mod p case. Given a finite group G, E a finite extension of Qp with ring of integers OE, uniformizer E, V a finite dimensional E-vector space and : G ! AutE(V ) an absolutely irreducible representation, we associate to the following directed graph: its vertices are homothety classes of lattices in V and there is an edge from (the class of) to 0 when E 0 and the quotient =0
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Alam, Muhammad Jawaherul. "Contact Representations of Graphs in 2D and 3D." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556963.

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We study contact representations of graphs in the plane and in 3D space, where vertices are represented by polygons or polyhedra and each edge is represented by a common boundary between two polygons or polyhedra. In the weighted version of the problem, we find contact representations with the additional restriction that the areas for the polygons or the volumes for the polyhedra realize some pre-specified value for the vertices. We address different variants of the problem depending on the types of polygons or polyhedra (convex or non-convex, axis-aligned or not), types of contacts (proper co
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Turner, Nicole. "Tropical Arithmetics and Dot Product Representations of Graphs." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4460.

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In tropical algebras we substitute min or max for the typical addition and then substitute addition for multiplication. A dot product representation of a graph assigns each vertex of the graph a vector such that two edges are adjacent if and only if the dot product of their vectors is greater than some chosen threshold. The resultS of creating dot product representations of graphs using tropical algebras are examined. In particular we examine the tropical dot product dimensions of graphs and establish connections to threshold graphs and the threshold dimension of a graph.
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Hartman, Gregory Neil. "Graphs and Noncommutative Koszul Algebras." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27156.

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A new connection between combinatorics and noncommutative algebra is established by relating a certain class of directed graphs to noncommutative Koszul algebras. The directed graphs in this class are called full graphs and are defined by a set of criteria on the edges. The structural properties of full graphs are studied as they relate to the edge criteria. A method is introduced for generating a Koszul algebra Lambda from a full graph G. The properties of Lambda are examined as they relate to the structure of G, with special attention being given to the construction of a projective resoluti
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Books on the topic "Representations of graphs"

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Praeger, Cheryl E. Low rank representations and graphs for sporadic groups. Cambridge University Press, 1996.

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Praeger, Cheryl E. Low rank representations and graphs for sporadic groups. Cambridge University Press, 1997.

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A course on the Web graph. American Mathematical Society, 2008.

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Peña, José Antonio de la, 1958-, Vallejo Ernesto 1959-, and Atakishiyev Natig M, eds. Algebraic structures and their representations: XV Coloquio Latinoamericano de Algebra, Cocoyoc, Morelos, México, July 20-26, 2003. American Mathematical Society, 2005.

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GRMS, or, Graphical representation of model spaces. Springer-Verlag, 1986.

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Isakov, Vladimir. Speak the language of schemes. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways
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Marie-Laure, Mugnier, ed. Graph-based knowledge representation: Computational foundations of conceptual graphs. Springer, 2009.

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Marie-Laure, Mugnier, ed. Graph-based knowledge representation: Computational foundations of conceptual graphs. Springer, 2009.

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Mineau, Guy W., Bernard Moulin, and John F. Sowa, eds. Conceptual Graphs for Knowledge Representation. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56979-0.

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Hamilton, William L. Graph Representation Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01588-5.

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Book chapters on the topic "Representations of graphs"

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Aldous, Joan M., and Robin J. Wilson. "Matrix Representations." In Graphs and Applications. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0467-4_5.

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Wallgrün, Jan Oliver. "Voronoi-Based Spatial Representations." In Hierarchical Voronoi Graphs. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_3.

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L'hostis, Alain. "Graph Theory and Representation of Distances: Chronomaps and Other Representations." In Graphs and Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118595473.ch9.

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Loebl, Martin. "Geometric representations of graphs." In Discrete Mathematics in Statistical Physics. Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9329-1_5.

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Munro, J. Ian, and Patrick K. Nicholson. "Compressed Representations of Graphs." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_646.

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Harborth, Heiko, and Arnfried Kemnitz. "Fibonacci Representations of Graphs." In Applications of Fibonacci Numbers. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3586-3_16.

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Munro, J. Ian, and Patrick K. Nicholson. "Compressed Representations of Graphs." In Encyclopedia of Algorithms. Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_646-1.

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Lozano, Miguel Angel, and Francisco Escolano. "Local Entropic Graphs for Globally-Consistent Graph Matching." In Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31988-7_33.

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Di Battista, Giuseppe, Fabrizio Frati, and Maurizio Patrignani. "Non-convex Representations of Graphs." In Graph Drawing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00219-9_38.

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Babilon, Robert, Helena Nyklová, Ondřej Pangrác, and Jan Vondrák. "Visibility Representations of Complete Graphs." In Graph Drawing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46648-7_34.

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Conference papers on the topic "Representations of graphs"

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Klisura, Ðorže. "Embedding Non-planar Graphs: Storage and Representation." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.13.

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In this paper, we propose a convention for repre-senting non-planar graphs and their least-crossing embeddings in a canonical way. We achieve this by using state-of-the-art tools such as canonical labelling of graphs, Nauty’s Graph6 string and combinatorial representations for planar graphs. To the best of our knowledge, this has not been done before. Besides, we implement the men-tioned procedure in a SageMath language and compute embeddings for certain classes of cubic, vertex-transitive and general graphs. Our main contribution is an extension of one of the graph data sets hosted on MathDat
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Alhomidi, Mohammed A., and Martin J. Reed. "Attack graphs representations." In 2012 4th Computer Science and Electronic Engineering Conference (CEEC). IEEE, 2012. http://dx.doi.org/10.1109/ceec.2012.6375383.

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Müller, Andreas, and Offer Shai. "A Unified Concept for the Graph Representation of Constraints in Mechanisms." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34713.

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There are two established approaches to represent constraints: the body-bar (BB) and the bar-joint (BJ) graph that can be used in machine theory. They are referred to as topological graphs as they describe the relation between members of a mechanism. It is known, however, that in many cases these graphs are not unique. Hence any method for kinematic analysis or mobility determination that is based on these topological graphs is prone to failures. In this paper a generalized and unified concept for the representation of constraints in mechanisms is introduced. It is first shown in which situati
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Hu, Binbin, Zhengwei Wu, Jun Zhou, et al. "MERIT: Learning Multi-level Representations on Temporal Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/288.

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Recently, representation learning on temporal graphs has drawn increasing attention, which aims at learning temporal patterns to characterize the evolving nature of dynamic graphs in real-world applications. Despite effectiveness, these methods commonly ignore the individual- and combinatorial-level patterns derived from different types of interactions (e.g.,user-item), which are at the heart of the representation learning on temporal graphs. To fill this gap, we propose MERIT, a novel multi-level graph attention network for inductive representation learning on temporal graphs.We adaptively em
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Molokwu, Bonaventure C., and Ziad Kobti. "Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/739.

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Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph
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Jin, Di, Luzhi Wang, Yizhen Zheng, et al. "CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/292.

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Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To th
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Song, Ying, Shuangjia Zheng, Zhangming Niu, Zhang-hua Fu, Yutong Lu, and Yuedong Yang. "Communicative Representation Learning on Attributed Molecular Graphs." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/392.

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Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling. Albeit powerful, the one-sided focuses on atom (node) or bond (edge) information of existing MPNN methods lead to the insufficient representations of the attributed molecular graphs. Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by stre
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Lopes, Juan P. A., Fabiano S. Oliveira, and Paulo E. D. Pinto. "Probabilistic data structures applied to implicit graph representation." In XXXI Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/ctd.2018.3659.

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In recent years, probabilistic data structures have been extensively employed to handle large volumes of streaming data in a timely fashion. However, their use in algorithms on giant graphs has been poorly explored. We introduce the concept of probabilistic implicit graph representation, which can represent large graphs using much less memory asymptotically by allowing adjacency test to have a constant probability of false positives or false negatives. This is an extension from the concept of implicit graph representation, comprehensively studied by Muller and Spinrad. Based on that, we also i
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White, D., and R. C. Wilson. "Mixing spectral representations of graphs." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.803.

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Kim, Seoyoon, Seongjun Yun, and Jaewoo Kang. "DyGRAIN: An Incremental Learning Framework for Dynamic Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/438.

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Graph-structured data provide a powerful representation of complex relations or interactions. Many variants of graph neural networks (GNNs) have emerged to learn graph-structured data where underlying graphs are static, although graphs in various real-world applications are dynamic (e.g., evolving structure). To consider the dynamic nature that a graph changes over time, the need for applying incremental learning (i.e., continual learning or lifelong learning) to the graph domain has been emphasized. However, unlike incremental learning on Euclidean data, graph-structured data contains depende
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Reports on the topic "Representations of graphs"

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Levandoski, J., and G. Abdulla. Temporal Representation in Semantic Graphs. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/923616.

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Karamchetty, Som. Natural Computing: Analysis of Graphs for Computer Representation. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada375557.

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Gross, Jonathan L. Topological Representation of Graph Isomorphism Types. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada243528.

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Delugach, Harry S., Lissa C. Cox, and David J. Skipper. Dependency Language Representation Using Conceptual Graphs. Autonomic Information Systems. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada399504.

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Vaidya, Nitin. Matrix Representation of Iterative Approximate Byzantine Consensus in Directed Graphs. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada558910.

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Madhusudan, T. N. A Review of Bond-Graph Representation Based Design Methodologies. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada311292.

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Chen, Yirng-An, and Randal E. Bryant. PBHD: An Efficient Graph Representation for Floating Point Circuit Verification,. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada327995.

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Mathuria, Aakanksha. Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7453.

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Bowyer, Kevin. Development of the Aspect Graph Representation for Use in Robot Vision. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada247109.

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Martin, S. P. The graph representation approach to topological field theory in 2 + 1 dimensions. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5812219.

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