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Search Results: 1 - 10 of 297561 matches for " J. Saramaki "
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Scale Free Networks from Self-Organisation
T. S. Evans,J. P. Saramaki
Physics , 2004, DOI: 10.1103/PhysRevE.72.026138
Abstract: We show how scale-free degree distributions can emerge naturally from growing networks by using random walks for selecting vertices for attachment. This result holds for several variants of the walk algorithm and for a wide range of parameters. The growth mechanism is based on using local graph information only, so this is a process of self-organisation. The standard mean-field equations are an excellent approximation for network growth using these rules. We discuss the effects of finite size on the degree distribution, and compare analytical results to simulated networks. Finally, we generalise the random walk algorithm to produce weighted networks with power-law distributions of both weight and degree.
Limited resolution and multiresolution methods in complex network community detection
J. M. Kumpula,J. Saramaki,K. Kaski,J. Kertesz
Physics , 2007, DOI: 10.1117/12.725560
Abstract: Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernandez, and Gomez. These methods are studied analytically, and applied to three test networks using simulated annealing.
Emergence of communities in weighted networks
J. M. Kumpula,J. -P. Onnela,J. Saramaki,K. Kaski,J. Kertesz
Physics , 2007, DOI: 10.1103/PhysRevLett.99.228701
Abstract: Topology and weights are closely related in weighted complex networks and this is reflected in their modular structure. We present a simple network model where the weights are generated dynamically and they shape the developing topology. By tuning a model parameter governing the importance of weights, the resulting networks undergo a gradual structural transition from a module free topology to one with communities. The model also reproduces many features of large social networks, including the "weak links" property.
Generalizations of the clustering coefficient to weighted complex networks
J. Saramaki,M. Kivela,J. -P. Onnela,K. Kaski,J. Kertesz
Physics , 2006, DOI: 10.1103/PhysRevE.75.027105
Abstract: The recent high level of interest in weighted complex networks gives rise to a need to develop new measures and to generalize existing ones to take the weights of links into account. Here we focus on various generalizations of the clustering coefficient, which is one of the central characteristics in the complex network theory. We present a comparative study of the several suggestions introduced in the literature, and point out their advantages and limitations. The concepts are illustrated by simple examples as well as by empirical data of the world trade and weighted coauthorship networks.
Spectrum, Intensity and Coherence in Weighted Networks of a Financial Market
G. Tibely,J. -P. Onnela,J. Saramaki,K. Kaski,J. Kertesz
Physics , 2006, DOI: 10.1016/j.physa.2006.04.042
Abstract: We construct a correlation matrix based financial network for a set of New York Stock Exchange (NYSE) traded stocks with stocks corresponding to nodes and the links between them added one after the other, according to the strength of the correlation between the nodes. The eigenvalue spectrum of the correlation matrix reflects the structure of the market, which also shows in the cluster structure of the emergent network. The stronger and more compact a cluster is, the earlier the eigenvalue representing the corresponding business sector occurs in the spectrum. On the other hand, if groups of stocks belonging to a given business sector are considered as a fully connected subgraph of the final network, their intensity and coherence can be monitored as a function of time. This approach indicates to what extent the business sector classifications are visible in market prices, which in turn enables us to gauge the extent of group-behaviour exhibited by stocks belonging to a given business sector.
Anomalous lifetime distributions and topological traps in ordering dynamics
X. Castello,R. Toivonen,V. M. Eguiluz,J. Saramaki,K. Kaski,M. San Miguel
Physics , 2007, DOI: 10.1209/0295-5075/79/66006
Abstract: We address the role of community structure of an interaction network in ordering dynamics, as well as associated forms of metastability. We consider the voter and AB model dynamics in a network model which mimics social interactions. The AB model includes an intermediate state between the two excluding options of the voter model. For the voter model we find dynamical metastable disordered states with a characteristic mean lifetime. However, for the AB dynamics we find a power law distribution of the lifetime of metastable states, so that the mean lifetime is not representative of the dynamics. These trapped metastable states, which can order at all time scales, originate in the mesoscopic network structure.
The International Trade Network: weighted network analysis and modelling
K. Bhattacharya,G. Mukherjee,J. Saramaki,K. Kaski,S. S. Manna
Quantitative Finance , 2007, DOI: 10.1088/1742-5468/2008/02/P02002
Abstract: Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN.
Structure and tie strengths in mobile communication networks
J. -P. Onnela,J. Saramaki,J. Hyvonen,G. Szabo,D. Lazer,K. Kaski,J. Kertesz,A. -L. Barabasi
Physics , 2006, DOI: 10.1073/pnas.0610245104
Abstract: Electronic databases, from phone to emails logs, currently provide detailed records of human communication patterns, offering novel avenues to map and explore the structure of social and communication networks. Here we examine the communication patterns of millions of mobile phone users, allowing us to simultaneously study the local and the global structure of a society-wide communication network. We observe a coupling between interaction strengths and the network's local structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties, but fall apart following a phase transition if the weak ties are removed. We show that this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities, and find that when it comes to information diffusion, weak and strong ties are both simultaneously ineffective.
Scale-Free Networks Generated By Random Walkers
Jari Saramaki,Kimmo Kaski
Physics , 2004, DOI: 10.1016/j.physa.2004.04.110
Abstract: We present a simple mechanism for generating undirected scale-free networks using random walkers, where the network growth is determined by choosing parent vertices by sequential random walks. We show that this mechanism produces scale-free networks with degree exponent gamma=3 and clustering coefficients depending on random walk length. The mechanism can be interpreted in terms of preferential attachment without explicit knowledge of node degrees.
Exploring Temporal Networks with Greedy Walks
Jari Saramaki,Petter Holme
Computer Science , 2015,
Abstract: Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timings of node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -- in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.
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