Random walks in directed modular networks

Because diffusion typically involves symmetric interactions, scant attention has been focused on studying asymmetric cases. However, important networked systems underlain by diffusion (e.g. cortical networks and WWW) are inherently directed. In the case of undirected diffusion, it can be shown that the steady-state probability of the random walk dynamics is fully correlated with the degree, which no longer holds for directed networks. We investigate the relationship between such probability and the inward node degree, which we call efficiency, in modular networks. Our findings show that the efficiency of a given community depends mostly on the balance between its ingoing and outgoing connections. In addition, we derive analytical expressions to show that the internal degree of the nodes do not play a crucial role in their efficiency, when considering the Erd\H{o}s-R\'enyi and Barab\'asi-Albert models. The results are illustrated with respect to the macaque cortical network, providing subsidies for improving transportation and communication systems

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.FAPESP - Fundacao de Amparo a Pesquisa do Estado de Sao Paulo [05/00587-5, 06/61743-7]Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)CNPq - Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [301303/06-1

Correlations between structure and dynamics in complex networks

Previous efforts in complex networks research focused mainly on the topological features of such networks, but now also encompass the dynamics. In this Letter we discuss the relationship between structure and dynamics, with an emphasis on identifying whether a topological hub, i.e. a node with high degree or strength, is also a dynamical hub, i.e. a node with high activity. We employ random walk dynamics and establish the necessary conditions for a network to be topologically and dynamically fully correlated, with topological hubs that are also highly active. Zipf's law is then shown to be a reflection of the match between structure and dynamics in a fully correlated network, as well as a consequence of the rich-get-richer evolution inherent in scale-free networks. We also examine a number of real networks for correlations between topology and dynamics and find that many of them are not fully correlated.Comment: 16 pages, 7 figures, 1 tabl

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions are welcome

Development of techniques based on complex networks for extractive text summarization

A Sumarização Automática de Textos tem considerável importância nas tarefas de localização e utilização de conteúdo relevante em meio à quantidade enorme de informação disponível atualmente em meio digital. Nessa área, procura-se desenvolver técnicas que possibilitem obter o conteúdo mais relevante de documentos, de maneira condensada, sem alterar seu significado original, e com mínima intervenção humana. O objetivo deste trabalho de mestrado foi investigar de que maneira conceitos desenvolvidos na área de Redes Complexas podem ser aplicados à Sumarização Automática de Textos, mais especificamente à sumarização extrativa. Embora grande parte das pesquisas em sumarização tenha se voltado para a utilização de técnicas extrativas, ainda é possível melhorar o nível de informatividade dos extratos gerados automaticamente. Neste trabalho, textos foram representados como redes, das quais foram extraídas medidas tradicionalmente utilizadas na caracterização de redes complexas (por exemplo, coeficiente de aglomeração, grau hierárquico e índice de localidade), com o intuito de fornecer subsídios à seleção das sentenças mais significativas de um texto. Essas redes são formadas pelas sentenças (representadas pelos vértices) de um determinado texto, juntamente com as repetições (representadas pelas arestas) de substantivos entre sentenças após lematização. Cada método de sumarização proposto foi aplicado no córpus TeMário, de textos jornalísticos em português, e em córpus das conferências DUC, de textos jornalísticos em inglês. A avaliação desse estudo foi feita por meio da realização de quatro experimentos, fazendo-se uso de métodos de avaliação automática (Rouge-1 e Precisão/Cobertura de sentenças) e comparando-se os resultados com os de outros sistemas de sumarização extrativa. Os melhores sumarizadores propostos referem-se aos seguintes conceitos: d-anel, grau, k-núcleo e caminho mínimo. Foram obtidos resultados comparáveis aos dos melhores métodos de sumarização já propostos para o português, enquanto que, para o inglês, os resultados são menos expressivos.Automatic Text Summarization has considerably importance in tasks such as finding and using relevant content in the enormous amount of information available nowadays in digital media. The focus in this field is on the development of techniques that allow someone to obtain the most relevant content of documents, in a condensed way, preserving the original meaning and with little (or even none) human help. The purpose of this MSc project was to investigate a way of applying concepts borrowed from the studies of Complex Networks to the Automatic Text Summarization field, specifically to the task of extractive summarization. Although the majority of works in summarization have focused on extractive techniques, it is still possible to obtain better levels of informativity in extracts automatically generated. In this work, texts were represented as networks, from which the most significant sentences were selected through the use of ranking algorithms. Such networks are obtained from a text in the following manner: the sentences are represented as nodes, and an edge between two nodes is created if there is at least one repetition of a noun in both sentences, after the lemmatization step. Measurements typically employed in the characterization of complex networks, such as clustering coefficient, hierarchical degree and locality index, were used on the basis of the process of node (sentence) selection in order to build an extract. Each summarization technique proposed was applied to the TeMário corpus, which comprises newspaper articles in Portuguese, and to the DUC corpora, which comprises newspaper articles in English. Four evaluation experiments were carried out, by means of automatic evaluation measurements (Rouge-1 and sentence Precision/Recall) and comparison with the results obtained by other extractive summarization systems. The best summarizers are the ones based on the following concepts: d-ring, degree, k-core and shortest path. Performances comparable to the best summarization systems for Portuguese were achieved, whilst the results are less significant for English

Relationships between the structure of complex networks and the random walk, transport and synchronization dynamics

O relacionamento entre estrutura e dinâmica em redes complexas foi considerado utilizando-se uma ampla gama de diferentes técnicas. Diversas redes reais foram estudadas em termos das correlações entre grau e atividade. A medida de atividade é definida como a proporção de visitas por vértice no regime estacionário do passeio aleatório simples. O estudo desse tipo de correlação é importante pois pode fornecer subsídios para que uma propriedade dinâmica de um vértice possa ser obtida somente analisando-se seu(s) grau(s). O conceito de acessibilidade foi abordado nesse contexto, permitindo que fossem evidenciadas diferentes correlações, em redes como a WWW, de acordo com a intensidade de acessibilidade dos vértices. Propôs-se também um novo modelo de rede baseado no crescimento do número de vértices em que novas conexões são criadas com probabilidade proporcional à atividade de cada vértice. Esse modelo pode ser entendido como uma generalização do modelo de Barabási e Albert para redes com arestas direcionadas. Utilizando-se um conjunto de diversas medidas estruturais, mostrou-se que o novo modelo apresenta, entre outros modelos tradicionais de redes, a maior compatibilidade com três redes corticais. Foi também desenvolvido um método para caracterização da distribuição de subgrafos e seus inter-relacionamentos. O principal aspecto dessa metodologia é a expansão gradual dos subgrafos, desenvolvida para que os vértices que encontram-se fora de subgrafos possam ter suas relevâncias quantificadas em termos da importância no estabelecimento das conexões entre subgrafos. Experimentos para ilustração do método foram realizados utilizando-se quatro modelos de redes e cinco redes reais, e os resultados obtidos foram relacionados aos processos dinâmicos de transporte e de espalhamento. Outro tópico aqui considerado é o dos efeitos da amostragem de redes corticais, quantificados por meio de análise multivariada e classificação, fazendo uso de um conjunto de medidas estruturais de redes. Esses efeitos também foram mensurados em termos do comportamento dinâmico das redes (sincronização e acessibilidade). Simulações dos métodos de encefalografia MEG e EEG mostraram que as redes amostradas podem apresentar características bem diferentes das da rede original, principalmente no caso de amostras pequenas. Adicionalmente, a rede integrada da bactéria Escherichia coli foi analisada, a qual incorpora (i) regulação de transcrição gênica, (ii) vias metabólicas e de sinalização e (iii) interações entre proteínas. Outliers foram identificados no relacionamento entre grau e atividade, os quais representam reguladores globais de transcrição. Além disso, verificou-se que esses outliers são genes altamente expressos em diferentes condições, apresentando, portanto, uma natureza global no controle de diversos outros genes da célula.The relationship between structure and dynamics was addressed by employing a wide range of different approaches. First, the correlations between degree and activity were studied in various real-world networks. The activity is defined as the proportion of visits to each node in the steady-state regime of the simple random walk. This type of correlation can provide means to assess node activity only in terms of the degree. The concept of accessibility was included in this analysis, showing an intimate relationship (in networks such as the WWW) between the type of correlation and the level of accessibility observed on nodes. A new complex network model founded on growth was also proposed, with new connections being established proportionally to the current activity of each node. This model can be understood as a generalization of the Barabási-Albert model for directed networks. By using several topological measurements we showed that this new model provides, among several other traditional theoretical types of networks, the greatest compatibility with three real-world cortical networks. Additionally, we developed a novel approach considering non-overlapping subgraphs and their interrelationships and distribution through a given network. The main aspect of the methodology is a novel merging procedure developed to assess the relevance of nodes (in relation to the overall subgraph interconnectivity) lying outside subgraphs. Experiments were carried out on four types of network models and five instances of real-world networks, in order to illustrate the application of the method. Furthermore, these results were related to the properties of the transport and spreading processes. Other topic here addressed is the sampling problem in cortical networks. Effects of sampling were quantified using multivariate analysis and classifiers based on structural network measurements. Samples were also evaluated in terms of their dynamical behavior using a synchronization model and the measure of accessibility. By simulating MEG/EEG recordings it was found that sampled networks may substantially deviate from the respective original networks, mainly for small sample sizes. We also report an analysis of the integrated network of Escherichia coli, which incorporates (i) transcriptional regulatory interactions, (ii) metabolic/signaling feedback and (iii) protein-protein interactions. Network outliers, which represent global transcriptional regulators, were identified in the relationship between out-degree and activity. These outliers are highly and widely expressed across conditions, therefore supporting their global nature in controlling many genes in the cell

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved

Some issues on complex networks for author characterization

Abstract. This paper presents a modeling technique of texts as complex networks and the investigation of the correlation between the properties of such networks and author characteristics. In an experiment with several books from 8 authors, we show that the networks produced for each author tend to have specific features, which indicates that complex networks can capture author characteristics and, therefore, could be used for the traditional task of authorship identification.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics