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Search Results: 1 - 10 of 299517 matches for " J. Saram?ki "
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A Model for Social Networks
R. Toivonen,J. -P. Onnela,J. Saramki,J. Hyv?nen,K. Kaski
Physics , 2006, DOI: 10.1016/j.physa.2006.03.050
Abstract: Social networks are organized into communities with dense internal connections, giving rise to high values of the clustering coefficient. In addition, these networks have been observed to be assortative, i.e. highly connected vertices tend to connect to other highly connected vertices, and have broad degree distributions. We present a model for an undirected growing network which reproduces these characteristics, with the aim of producing efficiently very large networks to be used as platforms for studying sociodynamic phenomena. The communities arise from a mixture of random attachment and implicit preferential attachment. The structural properties of the model are studied analytically and numerically, using the $k$-clique method for quantifying the communities.
Intensity and coherence of motifs in weighted complex networks
J. -P. Onnela,J. Saramki,J. Kertész,K. Kaski
Physics , 2004, DOI: 10.1103/PhysRevE.71.065103
Abstract: The local structure of unweighted networks can be characterized by the number of times a subgraph appears in the network. The clustering coefficient, reflecting the local configuration of triangles, can be seen as a special case of this approach. In this Letter we generalize this method for weighted networks. We introduce subgraph intensity as the geometric mean of its link weights and coherence as the ratio of the geometric to the corresponding arithmetic mean. Using these measures, motif scores and clustering coefficient can be generalized to weighted networks. To demonstrate these concepts, we apply them to financial and metabolic networks and find that inclusion of weights may considerably modify the conclusions obtained from the study of unweighted characteristics.
Broad lifetime distributions for ordering dynamics in complex networks
R. Toivonen,X. Castelló,V. M. Eguíluz,J. Saramki,K. Kaski,M. San Miguel
Physics , 2008, DOI: 10.1103/PhysRevE.79.016109
Abstract: We search for conditions under which a characteristic time scale for ordering dynamics towards either of two absorbing states in a finite complex network of interactions does not exist. With this aim, we study random networks and networks with mesoscale community structure built up from randomly connected cliques. We find that large heterogeneity at the mesoscale level of the network appears to be a sufficient mechanism for the absence of a characteristic time for the dynamics. Such heterogeneity results in dynamical metastable states that survive at any time scale.
Temporal motifs in time-dependent networks
Lauri Kovanen,Márton Karsai,Kimmo Kaski,János Kertész,Jari Saramki
Computer Science , 2011, DOI: 10.1088/1742-5468/2011/11/P11005
Abstract: Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as networks of telecommunication, neural signal processing, biochemical reactions and human social interactions. We introduce the framework of temporal motifs to study the mesoscale topological-temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to colored directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network.
Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks
Lauri Kovanen,Kimmo Kaski,János Kertész,Jari Saramki
Computer Science , 2013, DOI: 10.1073/pnas.1307941110
Abstract: Electronic communication records provide detailed information about temporal aspects of human interaction. Previous studies have shown that individuals' communication patterns have complex temporal structure, and that this structure has system-wide effects. In this paper we use mobile phone records to show that interaction patterns involving multiple individuals have non-trivial temporal structure that cannot be deduced from a network presentation where only interaction frequencies are taken into account. We apply a recently introduced method, temporal motifs, to identify interaction patterns in a temporal network where nodes have additional attributes such as gender and age. We then develop a null model that allows identifying differences between various types of nodes so that these differences are independent of the network based on interaction frequencies. We find gender-related differences in communication patters, and show the existence of temporal homophily, the tendency of similar individuals to participate in interaction patterns beyond what would be expected on the basis of the network structure alone. We also show that temporal patterns differ between dense and sparse parts of the network. Because this result is independent of edge weights, it can be considered as an extension of Granovetter's hypothesis to temporal networks.
Using explosive percolation in analysis of real-world networks
Raj Kumar Pan,Mikko Kivel?,Jari Saramki,Kimmo Kaski,János Kertész
Computer Science , 2010, DOI: 10.1103/PhysRevE.83.046112
Abstract: We apply a variant of the explosive percolation procedure to large real-world networks, and show with finite-size scaling that the university class, ordinary or explosive, of the resulting percolation transition depends on the structural properties of the network as well as the number of unoccupied links considered for comparison in our procedure. We observe that in our social networks, the percolation clusters close to the critical point are related to the community structure. This relationship is further highlighted by applying the procedure to model networks with pre-defined communities.
Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
M. Karsai,M. Kivel?,R. K. Pan,K. Kaski,J. Kertész,A. -L. Barabási,J. Saramki
Physics , 2010, DOI: 10.1103/PhysRevE.83.025102
Abstract: Communication networks show the small-world property of short paths, but the spreading dynamics in them turns out slow. We follow the time evolution of information propagation through communication networks by using the SI model with empirical data on contact sequences. We introduce null models where the sequences are randomly shuffled in different ways, enabling us to distinguish between the contributions of different impeding effects. The slowing down of spreading is found to be caused mostly by weight-topology correlations and the bursty activity patterns of individuals.
Multiscale Analysis of Spreading in a Large Communication Network
Mikko Kivel?,Raj Kumar Pan,Kimmo Kaski,János Kertész,Jari Saramki,Márton Karsai
Computer Science , 2011, DOI: 10.1088/1742-5468/2012/03/P03005
Abstract: In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how some dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large scale time-stamped data on mobile phone calls, we extend earlier results that point out the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multi-scale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one.
Temporal Networks
Petter Holme,Jari Saramki
Physics , 2011, DOI: 10.1016/j.physrep.2012.03.001
Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks.
Contribution of ARLTS1 Cys148Arg (T442C) Variant with Prostate Cancer Risk and ARLTS1 Function in Prostate Cancer Cells
Sanna Siltanen, Tiina Wahlfors, Martin Schindler, Outi R. Saramki, John Patrick Mpindi, Leena Latonen, Robert L. Vessella, Teuvo L. J. Tammela, Olli Kallioniemi, Tapio Visakorpi, Johanna Schleutker
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0026595
Abstract: ARLTS1 is a recently characterized tumor suppressor gene at 13q14.3, a region frequently deleted in both sporadic and hereditary prostate cancer (PCa). ARLTS1 variants, especially Cys148Arg (T442C), increase susceptibility to different cancers, including PCa. In this study the role of Cys148Arg substitution was investigated as a risk factor for PCa using both genetic and functional analysis. Cys148Arg genotypes and expression of the ARLTS1 were explored in a large set of familial and unselected PCa cases, clinical tumor samples, xenografts, prostate cancer cell lines and benign prostatic hyperplasia (BPH) samples. The frequency of the variant genotype CC was significantly higher in familial (OR = 1.67, 95% CI = 1.08–2.56, P = 0.019) and unselected patients (OR = 1.52, 95% CI = 1.18–1.97, P = 0.001) and the overall risk was increased (OR = 1.54, 95% CI = 1.20–1.98, P = 0.0007). Additional analysis with clinicopathological data revealed an association with an aggressive disease (OR = 1.28, 95% CI = 1.05-∞, P = 0.02). The CC genotype of the Cys148Arg variant was also contributing to the lowered ARLTS1 expression status in lymphoblastoid cells from familial patients. In addition significantly lowered ARLTS1 expression was observed in clinical tumor samples compared to BPH samples (P = 0.01). The ARLTS1 co-expression signature based on previously published microarray data was generated from 1587 cancer samples confirming the low expression of ARLTS1 in PCa and showed that ARLTS1 expression was strongly associated with immune processes. This study provides strong confirmation of the important role of ARLTS1 Cys148Arg variant as a contributor in PCa predisposition and a potential marker for aggressive disease outcome.
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