343,575 research outputs found

    The hierarchical organisation and dynamics of complex networks

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    Complex networks offer flexible representations of complex heterogeneous real-world systems. They are often weighted, attributed, directed and/or dynamic. As such, gaining an overall understanding of information flow through these systems remains a challenging problem in the machine learning community. This thesis provides a comprehensive examination of the hierarchy inherent to many complex networks, with the following contributions: • The first algorithm to learn low dimensional non-Euclidean representations of attributed nodes in a weighted complex network. • The first algorithm to learn low dimensional non-Euclidean representations of attributed nodes in a directed complex network. • A framework to explore the multi-scale organization of meso-scopic architectures in signalling networks, allowing for the identification of statistically significant drug-able targets. Through these contributions, the work proposed in this thesis contributes towards a greater understanding of the hierarchy in the organization and dynamics of complex real-world systems

    The Dynamics of Multi-Modal Networks

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    The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains

    Macroinvertebrates assembly in a patchy environment: centrality measures for the spatial network of detritus-based communities

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    Spatial patterns influence the persistence of populations and communities, giving useful insights on the mechanisms that confer robustness to ecological networks. The mechanisms that regulate the spatial distribution of species are related to the ability of populations to respond to spatio-temporal variations of ecological conditions, contributing to network structure and dynamic of persisting communities. We applied the framework of complex network to study the colonization process of Phragmites australis leaf detritus in six different pools in the patchy aquatic environment of Tarquinia saltern (central Italy). We used the colonization data of macroinvertebrates on leaf detritus assigning a link between two taxa if they shared a common pool, and measured their positional importance within the network. We found high clustering and short path lenght among nodes that is representative of small-world pattern, showing the relationship between robustness and nodes synchronicity in network attachment dynamics. Here we show how the identification of local (individual use of substrates by macroinvertebrates) and global (network properties) patterns in community structure could be the key to better understand the ecology, evolution and management of complex ecological network

    Graphical Analysis of Social Group Dynamics

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    Identifying communities in social networks becomes an increasingly important research problem. Several methods for identifying such groups have been developed, however, qualitative analysis (taking into account the scale of the problem) still poses serious problems. This paper describes a tool for facilitating such an analysis, allowing to visualize the dynamics and supporting localization of different events (such as creation or merging of groups). In the final part of the paper, the experimental results performed using the benchmark data (Enron emails) provide an insight into usefulness of the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46; IEEE Computer Society, 201

    Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

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    Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks

    Modeling and control of complex dynamic systems: Applied mathematical aspects

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    The concept of complex dynamic systems arises in many varieties, including the areas of energy generation, storage and distribution, ecosystems, gene regulation and health delivery, safety and security systems, telecommunications, transportation networks, and the rapidly emerging research topics seeking to understand and analyse. Such systems are often concurrent and distributed, because they have to react to various kinds of events, signals, and conditions. They may be characterized by a system with uncertainties, time delays, stochastic perturbations, hybrid dynamics, distributed dynamics, chaotic dynamics, and a large number of algebraic loops. This special issue provides a platform for researchers to report their recent results on various mathematical methods and techniques for modelling and control of complex dynamic systems and identifying critical issues and challenges for future investigation in this field. This special issue amazingly attracted one-hundred-and eighteen submissions, and twenty-eight of them are selected through a rigorous review procedure

    Analysis of group evolution prediction in complex networks

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    In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well

    Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

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    This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic R\"{o}ssler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.Comment: 21 pages, 7 figures; Chaos, Solitons & Fractals (2012
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