Barabasilab-networkscience Study Notes 6-evolving networks

The first contact with complexity science is in a book called Think Complexity, Dr. Allen speaks very well of data structure and complexity science, Barabasi is a well-known complex network scientist, Barabasilab is a laboratory he dominates, The note here is about the curriculum introduced in the notes, of course, other people's courses are not open classes, so from the PPT can only see the backbone of things, to add, slider related books here can be found

There are probably a couple of lessons to do with hands-on labs, using some software or libraries to explore specific networks in person.

In the middle also asked another laboratory to teach about the nature of the network over time, changing the network, which basically said that many of the reality of the network is slowly evolving over time, because the PPT is very concise and then I did not find the relevant content in textbook, so I do not do a detailed introduction

this blog mainly introduces Evolving networks! (Evolving networks how to translate it?) Translation into model evolution is a little better) the nature of the evolution of the model mainly

Okay, let's take a look at what the wiki says.

Evolving Networks is Networks that change as a function of time. They is a natural extension of network science since almost all real world networks evolve over time, either by adding or Removing nodes or links over time. Often all of these processes occur simultaneously, such as in social networks where people make and lose friends over time , thereby creating and destroying edges, and some people become part of new social networks or leave their networks, Chang ing the nodes in the network. Evolving network concepts build on established network theory and is now being introduced to studying networks in many Diverse fields.

Evolving networks is a network collection of some columns that change over time, he is the natural extension of network science, generally also think is based on the Network Science Foundation theory, this series of notes mainly talk about network science

The BA network model mentioned in the previous blog is just a simplified model that makes the simplest assumptions, assuming that network growth is linear, and that biased links are linear, however these two assumptions cause the model to fail to capture the following properties:

1. Variations in the shape of the degree distribution
2. Variations in the degree exponent
3. The Size-independent clustering coefficient

The BA model explains the scale-free nature of the real-world network, and we have only made two simple assumptions. Now let's assume that if we adjust the BA model to describe most of the networks in the real world. If we're going to do this, then we're going to add in our model some of the mechanisms that we know happen in the real world: adding a link is not just a new node, the reconnection of the connection (like the breakup of the real world and the new Love), the deletion of the link (breaking up), There are also node deletions (such as people dead), constraints or optimizations.

OK, now let's introduce a new star: Bianconi-barabasi model

In addition to introducing this network model, it also introduces fitness model

In complex network theory, the fitness model is a model of the evolution of a network:how the links between node s change over time depends on the fitness of nodes. Fitter nodes attract more links at the expense of less fit nodes.

It has been used to model the network structure of the world Wide Web.

Barabasilab-networkscience Learning Note 6-evolving Networks