SEDFE: Un Sistema Experto para el Diagnóstico Fitosanitario del Espárrago usando Redes Bayesianas

Este artículo propone un sistema experto basado en el modelo probabilístico de redesBayesianas para el diagnóstico de plagas y enfermedades del espárrago, el cual, haceuso de la técnica de propagación de certeza basada en el algoritmo de paso de mensajesde Kim y Pearl para la actualización de nodos dentro de la red de diagnóstico. De estamanera se logra alcanzar resultados con un margen de diferencia de centésimas conrespecto al cálculo exacto obtenido con la tabla de distribución conjunta completausando un algoritmo de Enumeración. Además, el sistema experto logra establecerresultados coherentes de acuerdo a los patrones convencionales de cada germenpatógeno del espárrago y sus manifestaciones

Causation generalization through the identification of equivalent nodes in causal sparse graphs constructed from text using node similarity strategies

Causal Bayesian Graphs can be constructed from causal information in text. These graphs can be sparse because the cause or effect event can be expressed in various ways to represent the same information. This sparseness can corrupt inferences made on the graph. This paper proposes to reduce sparseness by merging: equivalent nodes and their edges. This paper presents a number of experiments that evaluates the applicability of node similarity techniques to detect equivalent nodes. The experiments found that techniques that rely upon combination of node contents and structural information are the most accurate strategies, specifically we have employed: 1. node name similarity and 2. combination of node name similarity and common neighbours (SMCN). In addition, the SMCN returns ”better” equivalent nodes than the string matching strategy.São Paulo Research Foundation (FAPESP) (grants 2013/12191-5, 2011/22749-8 and 2011/20451-1

Link prediction in graph construction for supervised and semi-supervised learning

Many real-world domains are relational in nature since they consist of a set of objects related to each other in complex ways. However, there are also flat data sets and if we want to apply graph-based algorithms, it is necessary to construct a graph from this data. This paper aims to: i) increase the exploration of graph-based algorithms and ii) proposes new techniques for graph construction from flat data. Our proposal focuses on constructing graphs using link prediction measures for predicting the existence of links between entities from an initial graph. Starting from a basic graph structure such as a minimum spanning tree, we apply a link prediction measure to add new edges in the graph. The link prediction measures considered here are based on structural similarity of the graph that improves the graph connectivity. We evaluate our proposal for graph construction in supervised and semi-supervised classification and we confirm the graphs achieve better accuracy.São Paulo Research Foundation (FAPESP) (grants: 2013/12191-5, 2011/21880-3 and 2011/22749-8

Lazy multi-label learning algorithms based on mutuality strategies

Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its labels (multi-label). The prediction is made by following a voting criteria within the multi-labels of the set of k-Nearest Neighbors of the new instance. This work proposes the use of two alternative strategies to identify the set of these examples: the Mutual and Not Mutual Nearest Neighbors rules, which have already been used by lazy single-learning algorithms. In this work, we use these strategies to extend the lazy multi-label algorithm BRkNN. An experimental evaluation carried out to compare both mutuality strategies with the original BRkNN algorithm and the well-known MLkNN lazy algorithm on 15 benchmark datasets showed that MLkNN presented the best predictive performance for the Hamming-Loss evaluation measure, although it was significantly outperformed by the mutuality strategies when F-Measure is considered. The best results of the lazy algorithms were also compared with the results obtained by the Binary Relevance approach using three different base learning algorithms.FAPESP (grants 2010/15992-0, 2011/02393-4, 2011/22749-8 and 2013/12191-5)CNPq (grant 151836/2013-2

Multilevel refinement based on neighborhood similarity

The multilevel graph partitioning strategy aims to reduce the computational cost of the partitioning algorithm by applying it on a coarsened version of the original graph. This strategy is very useful when large-scale networks are analyzed. To improve the multilevel solution, refinement algorithms have been used in the uncorsening phase. Typical refinement algorithms exploit network properties, for example minimum cut or modularity, but they do not exploit features from domain specific networks. For instance, in social networks partitions with high clustering coefficient or similarity between vertices indicate a better solution. In this paper, we propose a refinement algorithm (RSim) which is based on neighborhood similarity. We compare RSim with: 1. two algorithms from the literature and 2. one baseline strategy, on twelve real networks. Results indicate that RSim is competitive with methods evaluated for general domains, but for social networks it surpasses the competing refinement algorithms.CNPq (grant 151836-/2013-2)FAPESP (grants 2011/22749-8, 11/20451-1 and 2013/12191-5)CAPE

Music genre classification using traditional and relational approaches

Given the huge size of music collections available on the Web, automatic genre classification is crucial for the organization, search, retrieval and recommendation of music. Different kinds of features have been employed as input to classification models which have been shown to achieve high accuracy in classification scenarios under controlled environments. In this work, we investigate two components of the music genre classification process: a novel feature vector obtained directly from a description of the musical structure described in MIDI files (named as structural features), and the performance of relational classifiers compared to the traditional ones. Neither structural features nor relational classifiers have been previously applied to the music genre classification problem. Our hyphoteses are: (i) the structural features provide a more effective description than those currently employed in automatic music genre classification tasks, and (ii) relational classifiers can outperform traditional algorithms, as they operate on graph models of the data that embed information on the similarity between music tracks. Results from experiments carried out on a music dataset with unbalanced distribution of genres indicate these hypotheses are promising and deserve further investigation.São Paulo Research Foundation (FAPESP) (grants 2011/21880-3, 2011/14165-6, 2011/22749-8, 2012/24537-0 and 2013/12191-5)National Council for Scientific and Technological Development (CNPq) (grant 151836/2013-2

A Naïve Bayes model based on overlapping groups for link prediction in online social networks

Link prediction in online social networks is useful in numerous applications, mainly for recommendation. Recently, different approaches have considered friendship groups information for increasing the link prediction accuracy. Nevertheless, these approaches do not consider the different roles that common neighbors may play in the different overlapping groups that they belong to. In this paper, we propose a new approach that uses overlapping groups structural information for building a naïve Bayes model. From this proposal, we show three different measures derived from the common neighbors. We perform experiments for both unsupervised and supervised link prediction strategies considering the link imbalance problem. We compare sixteen measures in four well-known online social networks: Flickr, LiveJournal, Orkut and Youtube. Results show that our proposals help to improve the link prediction accuracy.São Paulo Research Foundation (FAPESP) (grants: 2013/12191-5, 2011/21880-3, 2011/23689-9 and 2011/22749-8

Mineração do comportamento de usuários em redes sociais baseadas em localização

Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications.Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real

Link prediction in complex networks using community structure information

Diferentes sistemas do mundo real podem ser representados por redes. As redes são estruturas nas quais seus vértices (nós) representam entidades e links representam relações entre essas entidades. Além disso, as redes caracterizam-se por ser estruturas dinâmicas, o que implica na rápida aparição e desaparição de entidades e seus relacionamentos. Nesse cenário, um dos problemas importantes a serem enfrentados no contexto das redes, é da predição de links, isto é, prever a ocorrência futura de um link ainda não existente entre dois vértices com base nas informações já existentes. A importância da predição de links deve-se ao fato de ter aplicações na recuperação de informação, identificação de interações espúrias e, ainda, na avaliação de mecanismos de evolução das redes. Para enfrentar o problema da predição de links, a maioria dos métodos utiliza informações da vizinhança topológica das redes para atribuir um valor que represente a probabilidade de conexão futura entre um par de vértices analisados. No entanto, recentemente têm aparecido métodos híbridos, caracterizados por usar outras informações além da vizinhança topológica, sendo as informações das comunidades as normalmente usadas, isso, devido ao fato que, ao serem grupos de vértices densamente ligados entre si e esparsamente ligados com vértices de outros grupos, fornecem informações que podem ser úteis para determinar o comportamento futuro das redes. Assim, neste trabalho são apresentadas duas propostas na linha dos métodos baseados nas informações das comunidades para predição de links. A primeira proposta consiste em um novo índice de similaridade que usa as informações dos vértices pertencentes a mesma comunidade na vizinhança de um par de vértices analisados, bem como as informações dos vértices pertencentes a diferentes comunidades nessa mesma vizinhança. A segunda proposta consiste de um conjunto de índices obtidos a partir da reformulação de algumas propostas já existentes, porém, inserindo neles informações dos vértices pertencentes unicamente à mesma comunidade na vizinhança topológica de um par de vértices analisados. Experimentos realizados em dez redes complexas de diferentes domínios demonstraram que, em geral, os índices propostos obtiveram desempenho superior às abordagens usuaisDifferent real-world systems can be represented as networks. Networks are structures in which vertices (nodes) represent entities and links represent relationships between these entities. Moreover, networks are dynamic structures, which implies rapid appearance and disappearance of entities and their relationships. In this scenario, the link prediction problem attempts to predict the future existence of a link between a pair of vertices considering existing information. The link prediction importance is due to the fact of having different applications in areas such as information retrieval, identification of spurious interactions, as well as for understanding mechanisms of network evolution. To address the link prediction problem, many proposals use topological information to assign a value that represents the likelihood of a future connection between a pair of vertices. However, hybrid methods have appeared recently. These methods use additional information such as community information. Communities are groups of vertices densely connected among them and sparsely connected to vertices from other groups, providing useful information to determinate the future behavior of networks. So, this research presents two proposals for link prediction based on communities information. The first proposal consists of a new similarity index that uses information about the communities that the vertices in the neighborhood of a analyzed pair of vertices belong. The second proposal is a set of indices obtained from the reformulation of various existing proposals, however, using only the information from vertices belonging to the same community in the neighborhood of a pair of vertices analyzed. Experiments conducted in ten complex networks of different fields show the proposals outperform traditional approache

[pt] SEGUNDA LISTA DE EXERCÍCIOS - ECO1109 - 2005.1

Many real world complex networks have an a overlapping community structure, in which a vertex belongs to one or more communities. Numerous approaches for crisp overlapping community detection were proposed in the literature, most of them have a good accuracy but their computational costs are considerably high and infeasible for large-scale networks. Since the multilevel approach has not been previously applied to deal with overlapping communities detection problem, in this paper we propose an adaptation of this approach to tackle the detection problem to overlapping communities case. The goal is to analyze the time impact and the quality of solution of our multilevel strategy regarding to traditional algorithms. Our experiments show that our proposal consistently produces good performance compared to single-level algorithms and in less time.CNPq (grant: 151836/2013-2)FAPESP (grants: 2011/22749-8 and 2013/12191-5)CAPE