Quick Notes on Nodexl
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Directed Graph Algorithms
Directed Graph Algorithms CSE 373 Data Structures Readings • Reading Chapter 13 › Sections 13.3 and 13.4 Digraphs 2 Topological Sort 142 322 143 321 Problem: Find an order in which all these courses can 326 be taken. 370 341 Example: 142 Æ 143 Æ 378 Æ 370 Æ 321 Æ 341 Æ 322 Æ 326 Æ 421 Æ 401 378 421 In order to take a course, you must 401 take all of its prerequisites first Digraphs 3 Topological Sort Given a digraph G = (V, E), find a linear ordering of its vertices such that: for any edge (v, w) in E, v precedes w in the ordering B C A F D E Digraphs 4 Topo sort – valid solution B C Any linear ordering in which A all the arrows go to the right F is a valid solution D E A B F C D E Note that F can go anywhere in this list because it is not connected. Also the solution is not unique. Digraphs 5 Topo sort – invalid solution B C A Any linear ordering in which an arrow goes to the left F is not a valid solution D E A B F C E D NO! Digraphs 6 Paths and Cycles • Given a digraph G = (V,E), a path is a sequence of vertices v1,v2, …,vk such that: ›(vi,vi+1) in E for 1 < i < k › path length = number of edges in the path › path cost = sum of costs of each edge • A path is a cycle if : › k > 1; v1 = vk •G is acyclic if it has no cycles. -
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. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
Structural Graph Theory Meets Algorithms: Covering And
Structural Graph Theory Meets Algorithms: Covering and Connectivity Problems in Graphs Saeed Akhoondian Amiri Fakult¨atIV { Elektrotechnik und Informatik der Technischen Universit¨atBerlin zur Erlangung des akademischen Grades Doktor der Naturwissenschaften Dr. rer. nat. genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Dr. Rolf Niedermeier Gutachter: Prof. Dr. Stephan Kreutzer Gutachter: Prof. Dr. Marcin Pilipczuk Gutachter: Prof. Dr. Dimitrios Thilikos Tag der wissenschaftlichen Aussprache: 13. October 2017 Berlin 2017 2 This thesis is dedicated to my family, especially to my beautiful wife Atefe and my lovely son Shervin. 3 Contents Abstract iii Acknowledgementsv I. Introduction and Preliminaries1 1. Introduction2 1.0.1. General Techniques and Models......................3 1.1. Covering Problems.................................6 1.1.1. Covering Problems in Distributed Models: Case of Dominating Sets.6 1.1.2. Covering Problems in Directed Graphs: Finding Similar Patterns, the Case of Erd}os-P´osaproperty.......................9 1.2. Routing Problems in Directed Graphs...................... 11 1.2.1. Routing Problems............................. 11 1.2.2. Rerouting Problems............................ 12 1.3. Structure of the Thesis and Declaration of Authorship............. 14 2. Preliminaries and Notations 16 2.1. Basic Notations and Defnitions.......................... 16 2.1.1. Sets..................................... 16 2.1.2. Graphs................................... 16 2.2. Complexity Classes................................ -
Katz Centrality for Directed Graphs • Understand How Katz Centrality Is an Extension of Eigenvector Centrality to Learning Directed Graphs
Prof. Ralucca Gera, [email protected] Applied Mathematics Department, ExcellenceNaval Postgraduate Through Knowledge School MA4404 Complex Networks Katz Centrality for directed graphs • Understand how Katz centrality is an extension of Eigenvector Centrality to Learning directed graphs. Outcomes • Compute Katz centrality per node. • Interpret the meaning of the values of Katz centrality. Recall: Centralities Quality: what makes a node Mathematical Description Appropriate Usage Identification important (central) Lots of one-hop connections The number of vertices that Local influence Degree from influences directly matters deg Small diameter Lots of one-hop connections The proportion of the vertices Local influence Degree centrality from relative to the size of that influences directly matters deg C the graph Small diameter |V(G)| Lots of one-hop connections A weighted degree centrality For example when the Eigenvector centrality to high centrality vertices based on the weight of the people you are (recursive formula): neighbors (instead of a weight connected to matter. ∝ of 1 as in degree centrality) Recall: Strongly connected Definition: A directed graph D = (V, E) is strongly connected if and only if, for each pair of nodes u, v ∈ V, there is a path from u to v. • The Web graph is not strongly connected since • there are pairs of nodes u and v, there is no path from u to v and from v to u. • This presents a challenge for nodes that have an in‐degree of zero Add a link from each page to v every page and give each link a small transition probability controlled by a parameter β. u Source: http://en.wikipedia.org/wiki/Directed_acyclic_graph 4 Katz Centrality • Recall that the eigenvector centrality is a weighted degree obtained from the leading eigenvector of A: A x =λx , so its entries are 1 λ Thoughts on how to adapt the above formula for directed graphs? • Katz centrality: ∑ + β, Where β is a constant initial weight given to each vertex so that vertices with zero in degree (or out degree) are included in calculations. -
A Gentle Introduction to Deep Learning for Graphs
A Gentle Introduction to Deep Learning for Graphs Davide Bacciua, Federico Erricaa, Alessio Michelia, Marco Poddaa aDepartment of Computer Science, University of Pisa, Italy. Abstract The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is a tutorial introduction to the field of deep learn- ing for graphs. It favors a consistent and progressive presentation of the main concepts and architectural aspects over an exposition of the most recent liter- ature, for which the reader is referred to available surveys. The paper takes a top-down view of the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured in- formation processing. Moreover, it introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. We com- plement the methodological exposition with a discussion of interesting research challenges and applications in the field. Keywords: deep learning for graphs, graph neural networks, learning for structured data arXiv:1912.12693v2 [cs.LG] 15 Jun 2020 1. Introduction Graphs are a powerful tool to represent data that is produced by a variety of artificial and natural processes. A graph has a compositional nature, being a compound of atomic information pieces, and a relational nature, as the links defining its structure denote relationships between the linked entities. -
Graph Mining: Social Network Analysis and Information Diffusion
Graph Mining: Social network analysis and Information Diffusion Davide Mottin, Konstantina Lazaridou Hasso Plattner Institute Graph Mining course Winter Semester 2016 Lecture road Introduction to social networks Real word networks’ characteristics Information diffusion GRAPH MINING WS 2016 2 Paper presentations ▪ Link to form: https://goo.gl/ivRhKO ▪ Choose the date(s) you are available ▪ Choose three papers according to preference (there are three lists) ▪ Choose at least one paper for each course part ▪ Register to the mailing list: https://lists.hpi.uni-potsdam.de/listinfo/graphmining-ws1617 GRAPH MINING WS 2016 3 What is a social network? ▪Oxford dictionary A social network is a network of social interactions and personal relationships GRAPH MINING WS 2016 4 What is an online social network? ▪ Oxford dictionary An online social network (OSN) is a dedicated website or other application, which enables users to communicate with each other by posting information, comments, messages, images, etc. ▪ Computer science : A network that consists of a finite number of users (nodes) that interact with each other (edges) by sharing information (labels, signs, edge directions etc.) GRAPH MINING WS 2016 5 Definitions ▪Vertex/Node : a user of the social network (Twitter user, an author in a co-authorship network, an actor etc.) ▪Edge/Link/Tie : the connection between two vertices referring to their relationship or interaction (friend-of relationship, re-tweeting activity, etc.) Graph G=(V,E) symbols meaning V users E connections deg(u) node degree: -
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences Adrian Duşa, Dietrich Nelle, Günter Stock, and Gert G. Wagner (Eds.) Facing the Future: European Research Infrastructures for the Humanities and Social Sciences E d i t o r s : Adrian Duşa (SCI-SWG), Dietrich Nelle (BMBF), Günter Stock (ALLEA), and Gert. G. Wagner (RatSWD) ISBN 978-3-944417-03-5 1st edition © 2014 SCIVERO Verlag, Berlin SCIVERO is a trademark of GWI Verwaltungsgesellschaft für Wissenschaftspoli- tik und Infrastrukturentwicklung Berlin UG (haftungsbeschränkt). This book documents the results of the conference Facing the Future: European Research Infrastructure for Humanities and Social Sciences (November 21/22 2013, Berlin), initiated by the Social and Cultural Innovation Strategy Work- ing Group of ESFRI (SCI-SWG) and the German Federal Ministry of Education and Research (BMBF), and hosted by the European Federation of Academies of Sciences and Humanities (ALLEA) and the German Data Forum (RatSWD). Thanks and appreciation are due to all authors, speakers and participants of the conference, and all involved institutions, in particular the German Federal Ministry of Education and Research (BMBF). The ministry funded the conference and this subsequent publication as part of the Union of the German Academies of Sciences and Humanities’ project “Survey and Analysis of Basic Humanities and Social Science Research at the Science Academies Related Research Insti- tutes of Europe”. The views expressed in this publication are exclusively the opinions of the authors and not those of the German Federal Ministry of Education and Research. Editing: Dominik Adrian, Camilla Leathem, Thomas Runge, Simon Wolff Layout and graphic design: Thomas Runge Contents Preface . -
Extracting Insights from Differences: Analyzing Node-Aligned Social
Extracting Insights from Differences: Analyzing Node-aligned Social Graphs by Srayan Datta A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2019 Doctoral Committee: Associate Professor Eytan Adar, Chair Associate Professor Mike Cafarella Assistant Professor Danai Koutra Associate Professor Clifford Lampe Srayan Datta [email protected] ORCID iD: 0000-0002-5800-830X c Srayan Datta 2019 To my family and friends ii ACKNOWLEDGEMENTS There are several people who made this dissertation possible, first among this long list is my adviser, Eytan Adar. Pursuing a doctoral program after just finishing un- dergraduate studies can be a daunting task but Eytan made it easy with his patience, kindness, and guidance. I learned a lot from our collaborations and idle conversations and I am very grateful for that. I would like to extend my thanks to the rest of my thesis committee, Mike Ca- farella, Danai Koutra and Cliff Lampe for their suggestions and constructive feed- back. I would also like to thank the following faculty members, Daniel Romero, Ceren Budak, Eric Gilbert and David Jurgens for their long insightful conversations and suggestions about some of my projects. I would like to thank all of friends and colleagues who helped (as a co-author or through critique) or supported me through this process. This is an enormous list but I am especially thankful to Chanda Phelan, Eshwar Chandrasekharan, Sam Carton, Cristina Garbacea, Shiyan Yan, Hari Subramonyam, Bikash Kanungo, and Ram Srivatasa. I would like to thank my parents for their unwavering support and faith in me. -
Network Analysis with Nodexl
Social data: Advanced Methods – Social (Media) Network Analysis with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group [email protected] http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/ http://www.flickr.com/photos/amycgx/3119640267/ Collaboration networks are social networks SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) E • Measure at the individual node or group level – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) F G • -
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. -
Livro De Resumos EDITORA DA UNIVERSIDADE FEDERAL DE SERGIPE
Organizadores: Carlos Alexandre Borges Garcia Marcus Eugênio Oliveira Lima Livro de Resumos EDITORA DA UNIVERSIDADE FEDERAL DE SERGIPE COORDENADORA DO PROGRAMA EDITORIAL Messiluce da Rocha Hansen COORDENADOR GRÁFICO DA EDITORA UFS Germana Gonçalves de Araújo PROJETO GRÁFICO E EDITORAÇÃO ELETRÔNICA Alisson Vitório de Lima FOTOGRAFIAS Disponibilizadas sob licença Creative Commons, ou de domínio público. Adilson Andrade - Foto da página X; FICHA CATALOGRÁFICA ELABORADA PELA BIBLIOTECA CENTRAL UNIVERSIDADE FEDERAL DE SERGIPE Encontro de Pós-Graduação (8. : 2016 : São Cristóvão, SE) Livro de resumos [recurso eletrônico] : VIII Encontro de Pós-Graduação / organizadores: Carlos Alexandre Borges Garcia, Marcus Eugênio Oliveira Lima. – São Cristóvão : Editora UFS : Universidade E56l Federal de Sergipe, Programa de Pós-Graduação, 2016. 353 p. ISBN 978-85-7822-550-6 1. Pós-graduação – Congresso. I. Universidade Federal de Sergipe. II. Garcia, Carlos Alexandre Borges. III. Lima, Marcus Eugênio Oliveira. CDU 378.046-021.68 Cidade Universitária “Prof. José Aloísio de Campos” CEP 49.100-000 – São Cristóvão - SE. Telefone: 3194 - 6922/6923. e-mail: [email protected] http://livraria.ufs.br/ Este portfólio, ou parte dele, não pode ser reproduzido por qualquer meio sem autorização escrita da Editora. Organizadores: Carlos Alexandre Borges Garcia Marcus Eugênio Oliveira Lima Livro de Resumos UFS São Cristóvão/SE - 2016 Ciências Agrárias A (des)realização da estratégia democrático-popular: Uma análise a partir da realidade do movimento dos trabalhadores rurais sem terra (MST) e do Partido dos Trabalhadores (PT) Autor: SOUSA, Ronilson Barboza de. Orientador: RAMOS FILHO, Eraldo da Silva. A referente tese de doutorado, que vem sendo desenvolvida, tem como objetivo analisar o processo de realização da estratégia democrático-popular, adotada pelo Movimento dos Trabalhadores Rurais Sem Terra (MST) e pelo Partido dos Trabalhadores (PT), especial- mente na luta pela terra e pela reforma agrária.