Last version of Gephi introduced some very nice feature. It is now possible to work with dynamic networks that can easily be observed in their evolution. Working with dynamic networks is crucial when you are dealing with social networks, like those existing in microblogging sites, that show a high level of variability: social connections quickly change over time and – even if the connection does not disappear – the use of a specific connection can be very different from time to time. Observing such a phenomenon could be difficult with a static SNA but with a dynamic perspective it becomes quite simple.
The movie shows how the egonetwork of my Friendfeed user changed within the period Aug. – Sept. 2010. The ego-node represents my user and the other nodes are all the users I’ve interacted with (on FriendFeed). Nodes with a higher level of interaction are visualised closer to the ego-node while users with a low level of interaction are pushed away from the ego-node.
The video span over two month of time with the data-resolution set at 10 days (this creates 5 different configuration of the network) and it clearly shows how closer nodes change even in such a short amount of time.
Even if this was intended to be just a demo of then new opportunities offered by gephi it provides some insights about how ego-networks evolve over time. This evolution can be due to endogenous or exogenous aspects but it seems to be quicker than what one could expect.L’ultima versione di Gephi permette finalmente la visualizzazione di reti dinamiche. Quella che trovate qui è una breve visualizzazione di come l’ego-network attiva di uno specifico utente (in questo caso il mio utente FriendFeed) è cambiata nel corso dei mesi di Agosto-Settembre 2010.
I nodi sono posizionati – rispetto al nodo centrale – in modo da rappresentare il livello di interazione: i nodi più prossimi sono quelli con un livello di interazione maggiore. Com’è possibile vedere i nodi più prossimi – ovvero i nodi con i quali in quel periodo ho interagito maggiormente – cambiano con un’elevata frequenza costringendo la rete a riadattarsi di conseguenza.
Oltre alla dimostrazione di alcune delle possibilità offerte dalla nuova versione di gephi questa breve visualizzazione ci permette di capire come i contatti con i quali interagiamo – pur all’interno di un numero sicuramente interiore rispetto all’insieme delle connessioni possibili – sono una realtà dinamica in costante evoluzione. Le ragioni di questa evoluzione possono essere le più diverse, da fattori endogeni alla rete (discussioni interessanti) a fattori esogeni (eventi esterni che desideriamo trattare con alcuni contatti).
Tag: egonetwork
Mapping FriendFeed Network: switching the perspective
Recently we’ve posted a visualisation of the Italian Network of FriendFeed. Such a map was an interesting and general perspective showing how a complex network can be visualised. Obviously when we’re dealing with social networks or microblogging sites we’re dealing with a complex network emerging from many different egonetworks. How is the perspective over the same Network (Italian Friendfeed Users) if we observe it from the inside?
Starting from the same dataset we’ve used for the previous visualisation we generated an Egonetwork using as central user one of the minor nodes of the global visualisation. We chose the user lucamondini who, at that time, had a rather small network: 150 following and 200 followers. The idea was to see how the network looked like when the observer was one of the peripheral nodes.
An explanation of nodes sizes and colours can be found here, what’s interesting is that switching the perspective to this user’s point of view the overall scenario change. Even if it is always possible to find major and minor nodes they don’t seem to be necessarily the same nodes of the global map. Of course users that are very popular within the whole network seem to be quite popular also within local egonetwork but their specific size is different. Moving our observation to nodes that are even more peripheral (we chose the user magicabula, 21 followers and 33 following) – since it has so few followers this user wasn’t included into the global visualisation – will show a small network of highly connected users where some of the users are heavily connected even in the global map and some have a large authority only within this local perspective.
When we’re dealing with social network and information propagation we must keep in mind that the scenario might be really different when it is observed from the far periphery of the network.