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Structure and tie strengths in mobile communication networks
TLDR
We observe a coupling between interaction strengths and the network structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties but fall apart after a phase transition if the weak ties are removed. Expand
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Temporal Networks
TLDR
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. Expand
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Intensity and coherence of motifs in weighted complex networks.
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,Expand
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Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
TLDR
We follow the time evolution of information propagation through communication networks by using empirical data on contact sequences and the susceptible-infected model. Expand
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Characterizing the Community Structure of Complex Networks
TLDR
We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. Expand
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Temporal motifs in time-dependent networks
TLDR
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. Expand
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Persistence of social signatures in human communication
TLDR
We combine cell phone data with survey responses to show that a person’s social signature, as we call the pattern of their interactions with different friends and family members, is remarkably robust. Expand
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Generalizations of the clustering coefficient to weighted complex networks.
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 onExpand
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Sequential algorithm for fast clique percolation.
In complex network research clique percolation, introduced by Palla, Derényi, and Vicsek [Nature (London) 435, 814 (2005)], is a deterministic community detection method which allows for overlappingExpand
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A comparative study of social network models: Network evolution models and nodal attribute models
TLDR
This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). Expand
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