On Algebras and Matroids Associated to Undirected Graphs
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Beta Current Flow Centrality for Weighted Networks Konstantin Avrachenkov, Vladimir Mazalov, Bulat Tsynguev
Beta Current Flow Centrality for Weighted Networks Konstantin Avrachenkov, Vladimir Mazalov, Bulat Tsynguev To cite this version: Konstantin Avrachenkov, Vladimir Mazalov, Bulat Tsynguev. Beta Current Flow Centrality for Weighted Networks. 4th International Conference on Computational Social Networks (CSoNet 2015), Aug 2015, Beijing, China. pp.216-227, 10.1007/978-3-319-21786-4_19. hal-01258658 HAL Id: hal-01258658 https://hal.inria.fr/hal-01258658 Submitted on 19 Jan 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Beta Current Flow Centrality for Weighted Networks Konstantin E. Avrachenkov1, Vladimir V. Mazalov2, Bulat T. Tsynguev3 1 INRIA, 2004 Route des Lucioles, Sophia-Antipolis, France [email protected] 2 Institute of Applied Mathematical Research, Karelian Research Center, Russian Academy of Sciences, 11, Pushkinskaya st., Petrozavodsk, Russia, 185910 [email protected] 3 Transbaikal State University, 30, Aleksandro-Zavodskaya st., Chita, Russia, 672039 [email protected] Abstract. Betweenness centrality is one of the basic concepts in the analysis of social networks. Initial definition for the betweenness of a node in a graph is based on the fraction of the number of geodesics (shortest paths) between any two nodes that given node lies on, to the total number of the shortest paths connecting these nodes. -
Graph Varieties Axiomatized by Semimedial, Medial, and Some Other Groupoid Identities
Discussiones Mathematicae General Algebra and Applications 40 (2020) 143–157 doi:10.7151/dmgaa.1344 GRAPH VARIETIES AXIOMATIZED BY SEMIMEDIAL, MEDIAL, AND SOME OTHER GROUPOID IDENTITIES Erkko Lehtonen Technische Universit¨at Dresden Institut f¨ur Algebra 01062 Dresden, Germany e-mail: [email protected] and Chaowat Manyuen Department of Mathematics, Faculty of Science Khon Kaen University Khon Kaen 40002, Thailand e-mail: [email protected] Abstract Directed graphs without multiple edges can be represented as algebras of type (2, 0), so-called graph algebras. A graph is said to satisfy an identity if the corresponding graph algebra does, and the set of all graphs satisfying a set of identities is called a graph variety. We describe the graph varieties axiomatized by certain groupoid identities (medial, semimedial, autodis- tributive, commutative, idempotent, unipotent, zeropotent, alternative). Keywords: graph algebra, groupoid, identities, semimediality, mediality. 2010 Mathematics Subject Classification: 05C25, 03C05. 1. Introduction Graph algebras were introduced by Shallon [10] in 1979 with the purpose of providing examples of nonfinitely based finite algebras. Let us briefly recall this concept. Given a directed graph G = (V, E) without multiple edges, the graph algebra associated with G is the algebra A(G) = (V ∪ {∞}, ◦, ∞) of type (2, 0), 144 E. Lehtonen and C. Manyuen where ∞ is an element not belonging to V and the binary operation ◦ is defined by the rule u, if (u, v) ∈ E, u ◦ v := (∞, otherwise, for all u, v ∈ V ∪ {∞}. We will denote the product u ◦ v simply by juxtaposition uv. Using this representation, we may view any algebraic property of a graph algebra as a property of the graph with which it is associated. -
General Approach to Line Graphs of Graphs 1
DEMONSTRATIO MATHEMATICA Vol. XVII! No 2 1985 Antoni Marczyk, Zdzislaw Skupien GENERAL APPROACH TO LINE GRAPHS OF GRAPHS 1. Introduction A unified approach to the notion of a line graph of general graphs is adopted and proofs of theorems announced in [6] are presented. Those theorems characterize five different types of line graphs. Both Krausz-type and forbidden induced sub- graph characterizations are provided. So far other authors introduced and dealt with single spe- cial notions of a line graph of graphs possibly belonging to a special subclass of graphs. In particular, the notion of a simple line graph of a simple graph is implied by a paper of Whitney (1932). Since then it has been repeatedly introduc- ed, rediscovered and generalized by many authors, among them are Krausz (1943), Izbicki (1960$ a special line graph of a general graph), Sabidussi (1961) a simple line graph of a loop-free graph), Menon (1967} adjoint graph of a general graph) and Schwartz (1969; interchange graph which coincides with our line graph defined below). In this paper we follow another way, originated in our previous work [6]. Namely, we distinguish special subclasses of general graphs and consider five different types of line graphs each of which is defined in a natural way. Note that a similar approach to the notion of a line graph of hypergraphs can be adopted. We consider here the following line graphsi line graphs, loop-free line graphs, simple line graphs, as well as augmented line graphs and augmented loop-free line graphs. - 447 - 2 A. Marczyk, Z. -
P2k-Factorization of Complete Bipartite Multigraphs
P2k-factorization of complete bipartite multigraphs Beiliang Du Department of Mathematics Suzhou University Suzhou 215006 People's Republic of China Abstract We show that a necessary and sufficient condition for the existence of a P2k-factorization of the complete bipartite- multigraph )"Km,n is m = n == 0 (mod k(2k - l)/d), where d = gcd()", 2k - 1). 1. Introduction Let Km,n be the complete bipartite graph with two partite sets having m and n vertices respectively. The graph )"Km,n is the disjoint union of ).. graphs each isomorphic to Km,n' A subgraph F of )"Km,n is called a spanning subgraph of )"Km,n if F contains all the vertices of )"Km,n' It is clear that a graph with no isolated vertices is uniquely determined by the set of its edges. So in this paper, we consider a graph with no isolated vertices to be a set of 2-element subsets of its vertices. For positive integer k, a path on k vertices is denoted by Pk. A Pk-factor of )'Km,n is a spanning subgraph F of )"Km,n such that every component of F is a Pk and every pair of Pk's have no vertex in common. A Pk-factorization of )"Km,n is a set of edge-disjoint Pk-factors of )"Km,n which is a partition of the set of edges of )'Km,n' (In paper [4] a Pk-factorization of )"Km,n is defined to be a resolvable (m, n, k,)..) bipartite Pk design.) The multigraph )"Km,n is called Ph-factorable whenever it has a Ph-factorization. -
Trees and Graphs
Module 8: Trees and Graphs Theme 1: Basic Properties of Trees A (rooted) tree is a finite set of nodes such that ¯ there is a specially designated node called the root. ¯ d Ì ;Ì ;::: ;Ì ½ ¾ the remaining nodes are partitioned into disjoint sets d such that each of these Ì ;Ì ;::: ;Ì d ½ ¾ sets is a tree. The sets d are called subtrees,and the degree of the root. The above is an example of a recursive definition, as we have already seen in previous modules. A Ì Ì Ì B C ¾ ¿ Example 1: In Figure 1 we show a tree rooted at with three subtrees ½ , and rooted at , and D , respectively. We now introduce some terminology for trees: ¯ A tree consists of nodes or vertices that store information and often are labeled by a number or a letter. In Figure 1 the nodes are labeled as A;B;:::;Å. ¯ An edge is an unordered pair of nodes (usually denoted as a segment connecting two nodes). A; B µ For example, ´ is an edge in Figure 1. A ¯ The number of subtrees of a node is called its degree. For example, node is of degree three, while node E is of degree two. The maximum degree of all nodes is called the degree of the tree. Ã ; Ä; F ; G; Å ; Á Â ¯ A leaf or a terminal node is a node of degree zero. Nodes and are leaves in Figure 1. B ¯ A node that is not a leaf is called an interior node or an internal node (e.g., see nodes and D ). -
A Faster Parameterized Algorithm for PSEUDOFOREST DELETION
A faster parameterized algorithm for PSEUDOFOREST DELETION Citation for published version (APA): Bodlaender, H. L., Ono, H., & Otachi, Y. (2018). A faster parameterized algorithm for PSEUDOFOREST DELETION. Discrete Applied Mathematics, 236, 42-56. https://doi.org/10.1016/j.dam.2017.10.018 Document license: Unspecified DOI: 10.1016/j.dam.2017.10.018 Document status and date: Published: 19/02/2018 Document Version: Accepted manuscript including changes made at the peer-review stage Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
Sphere-Cut Decompositions and Dominating Sets in Planar Graphs
Sphere-cut Decompositions and Dominating Sets in Planar Graphs Michalis Samaris R.N. 201314 Scientific committee: Dimitrios M. Thilikos, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Supervisor: Stavros G. Kolliopoulos, Dimitrios M. Thilikos, Associate Professor, Professor, Dep. of Informatics and Dep. of Mathematics, National and Telecommunications, National and Kapodistrian University of Athens. Kapodistrian University of Athens. white Lefteris M. Kirousis, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Aposunjèseic sfairik¸n tom¸n kai σύνοla kuriarqÐac se epÐpeda γραφήματa Miχάλης Σάμαρης A.M. 201314 Τριμελής Epiτροπή: Δημήτρioc M. Jhlυκός, Epiblèpwn: Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. Δημήτρioc M. Jhlυκός, Staύρoc G. Kolliόποuloc, Kajhγητής tou Τμήμatoc Anaπληρωτής Kajhγητής, Tm. Plhroforiκής Majhmatik¸n tou PanepisthmÐou kai Thl/ni¸n, E.K.P.A. Ajhn¸n Leutèrhc M. Kuroύσης, white Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. PerÐlhyh 'Ena σημαντικό apotèlesma sth JewrÐa Γραφημάτwn apoteleÐ h apόdeixh thc eikasÐac tou Wagner από touc Neil Robertson kai Paul D. Seymour. sth σειρά ergasi¸n ‘Ελλάσσοna Γραφήματα’ apo to 1983 e¸c to 2011. H eikasÐa αυτή lèei όti sthn κλάση twn γραφημάtwn den υπάρχει άπειρη antialusÐda ¸c proc th sqèsh twn ελλασόnwn γραφημάτwn. H JewrÐa pou αναπτύχθηκε gia thn απόδειξη αυτής thc eikasÐac eÐqe kai èqei ακόμα σημαντικό antÐktupo tόσο sthn δομική όσο kai sthn algoriθμική JewrÐa Γραφημάτwn, άλλα kai se άλλα pedÐa όπως h Παραμετρική Poλυπλοκόthta. Sta πλάιsia thc απόδειξης oi suggrafeÐc eiσήγαγαν kai nèec paramètrouc πλά- touc. Se autèc ήτan h κλαδοαποσύνθεση kai to κλαδοπλάτoc ενός γραφήματoc. H παράμετρος αυτή χρησιμοποιήθηκε idiaÐtera sto σχεδιασμό algorÐjmwn kai sthn χρήση thc τεχνικής ‘διαίρει kai basÐleue’. -
Modeling Multiple Relationships in Social Networks
Asim AnsAri, Oded KOenigsberg, and FlOriAn stAhl * Firms are increasingly seeking to harness the potential of social networks for marketing purposes. therefore, marketers are interested in understanding the antecedents and consequences of relationship formation within networks and in predicting interactivity among users. the authors develop an integrated statistical framework for simultaneously modeling the connectivity structure of multiple relationships of different types on a common set of actors. their modeling approach incorporates several distinct facets to capture both the determinants of relationships and the structural characteristics of multiplex and sequential networks. they develop hierarchical bayesian methods for estimation and illustrate their model with two applications: the first application uses a sequential network of communications among managers involved in new product development activities, and the second uses an online collaborative social network of musicians. the authors’ applications demonstrate the benefits of modeling multiple relations jointly for both substantive and predictive purposes. they also illustrate how information in one relationship can be leveraged to predict connectivity in another relation. Keywords : social networks, online networks, bayesian, multiple relationships, sequential relationships modeling multiple relationships in social networks The rapid growth of online social networks has led to a social commerce (Stephen and Toubia 2010; Van den Bulte resurgence of interest in the marketing -
The Chromatic Index of Nearly Bipartite Multigraphs
JOURNAL OF COMBINATORIAL THEORY, Series B 40, 71-80 (1986) The Chromatic Index of Nearly Bipartite Multigraphs LARRY EGGAN AND MICHAEL PLANTHOLT Department of Mathematics, Illinois State University Normal, Illinois 61761 Communicated by the Editors Received January 5, 1984 We determine the chromatic index of any multigraph which contains a vertex whose deletion results in a bipartite multigraph. 0 1986Academic Press, Inc. 1. INTRODUCTION We denote the vertex and edge sets of a multigraph M by v(M) and E(M), respectively. An (edge) coloring of a multigraph is an assignment of colors to its edges in such a way that no two adjacent edges are assigned the same color. The chromatic index x’(M) of a multigraph M is the minimum number of colors used among all possible colorings of A4. Shan- non [ 13) proved that for any multigraph A4 with maximum degree d(M), d(M) <X’(M) d 1. d(M), while Vizing’s theorem [14] states that the chromatic index of a (simple) graph G is either d(G) or d(G) + 1. Although the general problem of determining the chromatic index of a graph (and therefore of multigraphs) is NP-complete (Holyer [6]), some good results have been obtained by considering special families of graphs of multigraphs where the x’ invariant behaves more predictably. For any multigraph A4, let t(M) = max,(2 IE(H)(/( 1V(H)1 - l)>, where the maximum is taken over all induced submultigraphs of A4 which have an odd number of vertices, and let 4(M) = max{d(M), [t(M)]). -
Analyzing Local and Global Properties of Multigraphs
The University of Manchester Research Analyzing local and global properties of multigraphs DOI: 10.1080/0022250X.2016.1219732 Document Version Accepted author manuscript Link to publication record in Manchester Research Explorer Citation for published version (APA): Shafie, T. (2016). Analyzing local and global properties of multigraphs. Journal of Mathematical Sociology, 40(4), 239-264. https://doi.org/10.1080/0022250X.2016.1219732 Published in: Journal of Mathematical Sociology Citing this paper Please note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version. General rights Copyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Takedown policy If you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [http://man.ac.uk/04Y6Bo] or contact [email protected] providing relevant details, so we can investigate your claim. Download date:30. Sep. 2021 Analysing Local and Global Properties of Multigraphs Termeh Shafie Department of Computer and Information Science University of Konstanz, Germany [email protected] Abstract The local structure of undirected multigraphs under two random multigraph models is analysed and compared. The first model generates multigraphs by randomly coupling pairs of stubs according to a fixed degree sequence so that edge assignments to vertex pair sites are dependent. -
A Multigraph Approach to Social Network Analysis
1 Introduction Network data involving relational structure representing interactions between actors are commonly represented by graphs where the actors are referred to as vertices or nodes, and the relations are referred to as edges or ties connecting pairs of actors. Research on social networks is a well established branch of study and many issues concerning social network analysis can be found in Wasserman and Faust (1994), Carrington et al. (2005), Butts (2008), Frank (2009), Kolaczyk (2009), Scott and Carrington (2011), Snijders (2011), and Robins (2013). A common approach to social network analysis is to only consider binary relations, i.e. edges between pairs of vertices are either present or not. These simple graphs only consider one type of relation and exclude the possibility for self relations where a vertex is both the sender and receiver of an edge (also called edge loops or just shortly loops). In contrast, a complex graph is defined according to Wasserman and Faust (1994): If a graph contains loops and/or any pairs of nodes is adjacent via more than one line the graph is complex. [p. 146] In practice, simple graphs can be derived from complex graphs by collapsing the multiple edges into single ones and removing the loops. However, this approach discards information inherent in the original network. In order to use all available network information, we must allow for multiple relations and the possibility for loops. This leads us to the study of multigraphs which has not been treated as extensively as simple graphs in the literature. As an example, consider a network with vertices representing different branches of an organ- isation. -
Classifying Multigraph Models of Secondary RNA Structure Using Graph-Theoretic Descriptors
International Scholarly Research Network ISRN Bioinformatics Volume 2012, Article ID 157135, 11 pages doi:10.5402/2012/157135 Research Article Classifying Multigraph Models of Secondary RNA Structure Using Graph-Theoretic Descriptors Debra Knisley,1, 2 Jeff Knisley,1, 2 Chelsea Ross,2 and Alissa Rockney2 1 Institute for Quantitative Biology, East Tennessee State University, Johnson City, TN 37614-0663, USA 2 Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614-0663, USA Correspondence should be addressed to Debra Knisley, [email protected] Received 26 August 2012; Accepted 11 September 2012 Academic Editors: J. Arthur and N. Lemke Copyright © 2012 Debra Knisley et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The prediction of secondary RNA folds from primary sequences continues to be an important area of research given the significance of RNA molecules in biological processes such as gene regulation. To facilitate this effort, graph models of secondary structure have been developed to quantify and thereby characterize the topological properties of the secondary folds. In this work we utilize a multigraph representation of a secondary RNA structure to examine the ability of the existing graph-theoretic descriptors to classify all possible topologies as either RNA-like or not RNA-like. We use more than one hundred descriptors and several different machine learning approaches, including nearest neighbor algorithms, one-class classifiers, and several clustering techniques. We predict that many more topologies will be identified as those representing RNA secondary structures than currently predicted in the RAG (RNA-As-Graphs) database.