Betweenness Centrality in Some Classes of Graphs

There are several centrality measures that have been introduced and studied for real world networks. They account for the different vertex characteristics that permit them to be ranked in order of importance in the network. Betweenness centrality is a measure of the influence of a vertex over the flow of information between every pair of vertices under the assumption that information primarily flows over the shortest path between them. In this paper we present betweenness centrality of some important classes of graphs.

Fair Sets of Some Classes of Graphs

Given a non empty set S of vertices of a graph, the partiality of a vertex with respect to S is the di erence between maximum and minimum of the distances of the vertex to the vertices of S. The vertices with minimum partiality constitute the fair center of the set. Any vertex set which is the fair center of some set of vertices is called a fair set. In this paper we prove that the induced subgraph of any fair set is connected in the case of trees and characterise block graphs as the class of chordal graphs for which the induced subgraph of all fair sets are connected. The fair sets of Kn, Km;n, Kn e, wheel graphs, odd cycles and symmetric even graphs are identi ed. The fair sets of the Cartesian product graphs are also discussed

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 5: Affiliation Networks

How can we analyze and understand affiliation networks? In this class, we will discuss properties of affiliation networks and we will investigate the use of Galois lattices for the exploration of structural patterns in bi-partite graphs. Optional : L.C. Freeman and D.R. White. Using Galois Lattices to Represent Network Data. Sociological Methodology, (23):127--146, (1993)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 4: Social Network Analysis

What are fundamental entities in social networks and what information is contained in social graphs? We will discuss some selected concepts in social network analysis, such as one- and two mode networks, prestige and centrality, and cliques, clans and clubs. Readings: Web tool predicts election results and stock prices, J. Palmer, New Scientist, 07 February (2008) [Protected Access] Optional: Social Network Analysis, Methods and Applications, S. Wasserman and K. Faust (1994)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

Social Networks and Small World phenomena

Social Networking tools like Facebook yield recognisable small world phenomena, that is particular kinds of social graphs that facilitate particular kinds of interaction and information exchange.

WAIS seminar: Provenance Analytics and Crowdsourcing

Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.

Secuenciamiento de actividades educativas orientado a la reutilización y la auto-organización en tutoría inteligente. ' 'Sequencing of learning activities oriented towards reuse and auto-organization for intelligent tutoring systems' '.

Se pretende la elaboración una plataforma capaz de implementar sistemas de tutoría inteligente, orientada al problema de la adaptación del secuenciamiento; la creación de una herramienta o método que defina secuenciamientos adaptativos de material educativo; el desarrollo, empleando la herramienta anterior, de uno o más tutores y adaptar las técnicas de inteligencia de enjambre al campo de los cursos impartidos a través de Internet, Elearning.. En primer lugar se analiza el estado de la cuestión, destacando los aspectos novedosos. A continuación se presentan las aportaciones de la investigación. Por último, se exponen las conclusiones y las líneas de investigación abiertas. También se incluyen apéndices con información adicional que puede ser relevante para algunos lectores. Se investiga el uso de técnicas de inteligencia de enjambre para obtener sistemas educativos robustos y con capacidad de autoorganización. Las lecciones aprendidas de los sistemas cristalizan en la creación de un módulo para SIT.. Las principales contribuciones son: el diseño de una plataforma para el desarrollo de Sistemas de Tutoría Inteligentes (ITS); se presenta una herramienta para la adaptación de secuencias de unidades de aprendizaje, grafos de secuenciamiento (Sequencing Graphs, SG) y se analizan las iniciativas actuales relacionadas con las técnicas de enjambre en educación. Estas técnicas tienen un interés especial para los sistemas de elearning dada su complejidad. .