Clustering based on Random Graph Model embedding Vertex Features

Large datasets with interactions between objects are common to numerous scientific fields (i.e. social science, internet, biology...). The interactions naturally define a graph and a common way to explore or summarize such dataset is graph clustering. Most techniques for clustering graph vertices just use the topology of connections ignoring informations in the vertices features. In this paper, we provide a clustering algorithm exploiting both types of data based on a statistical model with latent structure characterizing each vertex both by a vector of features as well as by its connectivity. We perform simulations to compare our algorithm with existing approaches, and also evaluate our method with real datasets based on hyper-textual documents. We find that our algorithm successfully exploits whatever information is found both in the connectivity pattern and in the features

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Constrained Clustering Based on the Link Structure of a Directed Graph

In many segmentation applications, data objects are often clustered based purely on attribute-level similarities. This practice has neglected the useful information that resides in the link structure among data objects and the valuable expert domain knowledge about the desirable cluster assignment. Link structure can carry worthy information about the similarity between data objects (e.g. citation), and we should also incorporate the existing domain information on preferred outcome when segmenting data. In this paper, we investigate the segmentation problem combining these three sources of information, which has not been addressed in the existing literature. We propose a segmentation method for directed graphs that incorporates the attribute values, link structure and expert domain information (represented as constraints). The proposed method combines these three types of information to achieve good quality segmentation on data which can be represented as a directed graph. We conducted comprehensive experiments to evaluate various aspects of our approach and demonstrate the effectiveness of our method

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

El análisis de cocitación como metodología de investigación en Bibliotecología y Ciencia de la Información

Se muestra la pertinencia y utilidad del análisis de cocitación como metodología de investigación en Bibliotecología y Ciencia de la Información, a partir de un análisis bibliométrico y revisión del contenido de los principales trabajos publicados sobre esta temática. Se describen las principales aplicaciones y posibles usos que pueden tener los resultados de este tipo de análisis. Se mencionan los métodos y técnicas más utilizados para el análisis y visualización de las estructuras de conocimiento de dominios científicos, y se presentan algunos de los modelos de mapas propuestos. Finalmente, se mencionan las ventajas del análisis de cocitación y sus principales limitacionesPonencia presentada en la Mesa 33: La sociedad en red: Bibliotecas, archivos y redes de informaciónFacultad de Humanidades y Ciencias de la Educació

User-Assisted Similarity Estimation for Searching Related Web Pages

ABSTRACT To utilize the similarity information hidden in the Web graph, we investigate the problem of adaptively retrieving related Web pages with user assistance. Given a definition of similarities between pages, it is intuitive to estimate that any similarity will propagate from page to page, inducing an implicit topical relatedness between pages. In this paper, we extract connected subgraphs from the whole graph that consists of all pairs of pages whose similarity scores are above a given threshold, and then sort the candidates of related pages by a novel rank measure which is based on the combination distances of a flexible hierarchical clustering. Moreover, due to the subjectivity of similarity values, we dynamically supply the ordering list of related pages according to a parameter adjusted by users. We show our approach effectively handles a set of pages originating from three related categories of Web hierarchies, such as Google Directory. The experiments with three similarity measures demonstrate that using in-link information is favorable while using a combination measure of in-links and out-links lowers the precision of identifying similar pages