334 research outputs found
Evolutionary Centrality and Maximal Cliques in Mobile Social Networks
This paper introduces an evolutionary approach to enhance the process of
finding central nodes in mobile networks. This can provide essential
information and important applications in mobile and social networks. This
evolutionary approach considers the dynamics of the network and takes into
consideration the central nodes from previous time slots. We also study the
applicability of maximal cliques algorithms in mobile social networks and how
it can be used to find the central nodes based on the discovered maximal
cliques. The experimental results are promising and show a significant
enhancement in finding the central nodes
Network science algorithms for mobile networks.
Network Science is one of the important and emerging fields in computer science and engineering that focuses on the study and analysis of different types of networks. The goal of this dissertation is to design and develop network science algorithms that can be used to study and analyze mobile networks. This can provide essential information and knowledge that can help mobile networks service providers to enhance the quality of the mobile services. We focus in this dissertation on the design and analysis of different network science techniques that can be used to analyze the dynamics of mobile networks. These techniques include evolutionary clustering, classification, discovery of maximal cliques, and evolutionary centrality algorithms. We proposed evolutionary clustering and evolutionary centrality algorithms that can be used to dynamically discover clusters and central nodes in mobile networks. Overall, the experimental results show that the proposed evolutionary algorithms are robust to short-term variations but reflects long-term trends and can be used effectively to analyze the dynamics of mobile networks
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
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
Graph Metrics for Temporal Networks
Temporal networks, i.e., networks in which the interactions among a set of
elementary units change over time, can be modelled in terms of time-varying
graphs, which are time-ordered sequences of graphs over a set of nodes. In such
graphs, the concepts of node adjacency and reachability crucially depend on the
exact temporal ordering of the links. Consequently, all the concepts and
metrics proposed and used for the characterisation of static complex networks
have to be redefined or appropriately extended to time-varying graphs, in order
to take into account the effects of time ordering on causality. In this chapter
we discuss how to represent temporal networks and we review the definitions of
walks, paths, connectedness and connected components valid for graphs in which
the links fluctuate over time. We then focus on temporal node-node distance,
and we discuss how to characterise link persistence and the temporal
small-world behaviour in this class of networks. Finally, we discuss the
extension of classic centrality measures, including closeness, betweenness and
spectral centrality, to the case of time-varying graphs, and we review the work
on temporal motifs analysis and the definition of modularity for temporal
graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and
Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201
Élőlények kollektív viselkedésének statisztikus fizikája = Statistical physics of the collective behaviour of organisms
Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Exploring the Non-vertical Component of Bacterial Evolution Using Network Structures
The central tree metaphor has been challenged over the last couple of decades
with the observation of incongruent trees derived largely from protein-coding genes in
prokaryotic genomes. There are an increasing number of evolutionary processes and
entities that confuse and confound the traditional understanding of evolution. As a
result, these processes and entities are very often omitted from phylogenetic studies
altogether.
In this thesis I attempt to uncover the importance of non-tree like evolution. I
discuss the types of genes that do not adhere to vertical patterns of inheritance such as
fusion genes and mobile genetic elements. Furthermore I explore the alternative of
using network structures in describing the evolutionary history of bacteria.
This thesis recounts two key uses of networks for revealing the less commonly
noted aspects of bacterial evolution. Firstly I present each stage in the development of
a new method for identifying fusions of unrelated genes from conception of the idea,
through the implementation to its application to data. Secondly I use networks of gene
sharing to elucidate patterns of divergence among a group of closely related bacteria
that would have once formed a single species cloud.
These studies reveal an abundance of the types of genes that contradict
traditional tree-thinking and support the notion that a strictly vertical view of
evolution is inadequate when describing bacterial relationships
Structure and dynamics of core-periphery networks
Recent studies uncovered important core/periphery network structures
characterizing complex sets of cooperative and competitive interactions between
network nodes, be they proteins, cells, species or humans. Better
characterization of the structure, dynamics and function of core/periphery
networks is a key step of our understanding cellular functions, species
adaptation, social and market changes. Here we summarize the current knowledge
of the structure and dynamics of "traditional" core/periphery networks,
rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery
structures with network modules, we discriminate between global and local
cores. The core/periphery network organization lies in the middle of several
extreme properties, such as random/condensed structures, clique/star
configurations, network symmetry/asymmetry, network
assortativity/disassortativity, as well as network hierarchy/anti-hierarchy.
These properties of high complexity together with the large degeneracy of core
pathways ensuring cooperation and providing multiple options of network flow
re-channelling greatly contribute to the high robustness of complex systems.
Core processes enable a coordinated response to various stimuli, decrease
noise, and evolve slowly. The integrative function of network cores is an
important step in the development of a large variety of complex organisms and
organizations. In addition to these important features and several decades of
research interest, studies on core/periphery networks still have a number of
unexplored areas.Comment: a comprehensive review of 41 pages, 2 figures, 1 table and 182
reference
Large-Scale Networks: Algorithms, Complexity and Real Applications
Networks have broad applicability to real-world systems, due to their ability to model and represent complex relationships. The discovery and forecasting of insightful patterns from networks are at the core of analytical intelligence in government, industry, and science. Discoveries and forecasts, especially from large-scale networks commonly available in the big-data era, strongly rely on fast and efficient network algorithms.
Algorithms for dealing with large-scale networks are the first topic of research we focus on in this thesis. We design, theoretically analyze and implement efficient algorithms and parallel algorithms, rigorously proving their worst-case time and space complexities. Our main contributions in this area are novel, parallel algorithms to detect k-clique communities, special network groups which are widely used to understand complex phenomena. The proposed algorithms have a space complexity which is the square root of that of the current state-of-the-art. Time complexity achieved is optimal, since it is inversely proportional to the number of processing units available. Extensive experiments were conducted to confirm the efficiency of the proposed algorithms, even in comparison to the state-of-the-art. We experimentally measured a linear speedup, substantiating the optimal performances attained.
The second focus of this thesis is the application of networks to discover insights from real-world systems. We introduce novel methodologies to capture cross correlations in evolving networks. We instantiate these methodologies to study the Internet, one of the most, if not the most, pervasive modern technological system. We investigate the dynamics of connectivity among Internet companies, those which interconnect to ensure global Internet access. We then combine connectivity dynamics with historical worldwide stock markets data, and produce graphical representations to visually identify high correlations. We find that geographically close Internet companies offering similar services are driven by common economic factors. We also provide evidence on the existence and nature of hidden factors governing the dynamics of Internet connectivity. Finally, we propose network models to effectively study the Internet Domain Name System (DNS) traffic, and leverage these models to obtain rankings of Internet domains as well as to identify malicious activities
On the topology Of network fine structures
Multi-relational dynamics are ubiquitous in many complex systems like transportations, social and biological. This thesis studies the two mathematical objects that encapsulate these relationships --- multiplexes and interval graphs. The former is the modern outlook in Network Science to generalize the edges in graphs while the latter was popularized during the 1960s in Graph Theory.
Although multiplexes and interval graphs are nearly 50 years apart, their motivations are similar and it is worthwhile to investigate their structural connections and properties. This thesis look into these mathematical objects and presents their connections.
For example we will look at the community structures in multiplexes and learn how unstable the detection algorithms are. This can lead researchers to the wrong conclusions. Thus it is important to get formalism precise and this thesis shows that the complexity of interval graphs is an indicator to the precision. However this measure of complexity is a computational hard problem in Graph Theory and in turn we use a heuristic strategy from Network Science to tackle the problem.
One of the main contributions of this thesis is the compilation of the disparate literature on these mathematical objects. The novelty of this contribution is in using the statistical tools from population biology to deduce the completeness of this thesis's bibliography. It can also be used as a framework for researchers to quantify the comprehensiveness of their preliminary investigations.
From the large body of multiplex research, the thesis focuses on the statistical properties of the projection of multiplexes (the reduction of multi-relational system to a single relationship network). It is important as projection is always used as the baseline for many relevant algorithms and its topology is insightful to understand the dynamics of the system.Open Acces
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