# “Clustershuck”

## A network growth model that naturally yields clusters and heterogeneity

This explorable illustrates how strong heterogeneities, cluster-like structures and high variability in node connectivities can naturally emerge in growing networks. The mechanism explored here is very simple: When a new node is added to the network it picks another target node at random. **Instead of linking to it, the new node establishes a link to one of the target’s neighbors.**

# “Jujujajáki networks”

## The emergence of communities in weighted networks

This explorable illustrates a dynamic network model that was designed to capture the emergence of community structures, heterogeneities and clusters that are frequently observed in social networks. Clusters are characterized by a high probability that a person’s *‘friends are also friends’*. In this model not only the connectivity evolves but also the strength of links between nodes. The model was orginally proposed by Jussi Kumpula, Jukka-Pekka Onnela, Jari Saramäki, János Kertész and Kimmo Kaski.

# “The Blob”

## A network's giant component

This explorable illustrates an important feature of complex networks: the emergence of the * giant component*. Networks often have multiple components. A component is a part of the network where we can find a path between any two nodes by traversing links.

# “Echo Chambers”

## A model for opinion dynamics

This explorable illustrates a network model that captures how opinion dynamics might work in a population and how clusters of uniform opinion might naturally emerge from an initially random system. The model is a variant of a model introduced by
P. Holme and M. Newman in a 2006 paper: *Nonequilibrium phase transition in the coevolution of networks and opinions*.

# “Knitworks”

## Growing complex networks

This explorable illustrates network growth based on * preferential attachment*, a variant of the

**Barabasi-Albert model**that was introduced to capture strong heterogeneities observed in many natural and technological networks. It has become a popular model for scale-free networks in nature.

Preferential attachment means that nodes that enter the network during a growth process preferentially connect to nodes with specific properties. In the original system, they preferentially connect to existing nodes that are already well connected, increasing their connectivity even further. This *rich get richer* effect generates networks in which a few nodes are very strongly connected and very many nodes poorly.