Weight thresholding is a simple technique that aims at reducing the number of edges in weighted networks that are otherwise too dense for the application of standard graph theoretical methods. We show that the group structure of real weighted networks is very robust under weight thresholding, as it is maintained even when most of the edges are removed. This appears to be related to the correlation between topology and weight that characterizes real networks. On the other hand, the behavior of other properties is generally system dependent
In order to conduct analyses of networked systems where connections between individuals take on a ra...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
AbstractWe study the structure of the networks in which connectedness and disconnectedness can be ex...
Nodes in real-world networks tend to cluster into densely connected groups, a property captured by t...
<p>Because network topologies can be difficult to decipher in large networks, here we illustrate the...
We review the main tools which allow for the statistical characterization of weighted networks. We t...
The linear threshold model is widely adopted as a classic prototype for studying contagion processes...
Here we report a comprehensive analysis of the robustness of seven high-quality real-world complex w...
Weights and directionality of the edges carry a large part of the information we can extract from a ...
In this report we offer the widest comparison of links removal (attack) strategies efficacy in impai...
The role of weight on the weighted networks is investigated by studying the effect of weight on comm...
Proceedings of the conference \"Complex networks: structure, function and processes\", Kolkata (Sate...
We present an approach to the analysis of weighted networks, by providing a straightforward generali...
Many real-world networks tend to be very dense. Particular examples of interest arise in the constru...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
In order to conduct analyses of networked systems where connections between individuals take on a ra...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
AbstractWe study the structure of the networks in which connectedness and disconnectedness can be ex...
Nodes in real-world networks tend to cluster into densely connected groups, a property captured by t...
<p>Because network topologies can be difficult to decipher in large networks, here we illustrate the...
We review the main tools which allow for the statistical characterization of weighted networks. We t...
The linear threshold model is widely adopted as a classic prototype for studying contagion processes...
Here we report a comprehensive analysis of the robustness of seven high-quality real-world complex w...
Weights and directionality of the edges carry a large part of the information we can extract from a ...
In this report we offer the widest comparison of links removal (attack) strategies efficacy in impai...
The role of weight on the weighted networks is investigated by studying the effect of weight on comm...
Proceedings of the conference \"Complex networks: structure, function and processes\", Kolkata (Sate...
We present an approach to the analysis of weighted networks, by providing a straightforward generali...
Many real-world networks tend to be very dense. Particular examples of interest arise in the constru...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
In order to conduct analyses of networked systems where connections between individuals take on a ra...
Real-world networks process structured connections since they have non-trivial vertex degree correla...
AbstractWe study the structure of the networks in which connectedness and disconnectedness can be ex...