Giulia Bertagnolli


PhD in Math @unitn and @CoMuNe Lab - FBK. Interested in Complex Networks and working on their Geometry!

16 April 2019

New demo available! Check the network depth demo!

by G. Bertagnolli

This is a small demo related to our (G. Bertagnolli, C. Agostinelli, M. De Domenico) recent work, Network depth: identifying median and contours in complex networks, arXiv:1904.05060.

Demo (it may need few seconds to load).

Network Scientists 2010

The Network Scientists 2010 networkdownload data – is a co-authorship network with nodes.

Embedding in

The Network Scientists 2010 network. Node colour and size depend on the Projected Tukey Depth w.r.t. diffusion distance, . Plots for and different values of $p$, the dimension of the embedding space.

Pressing the animation button deeps will scale the nodes size based on the percentile they belong to. We consider the following percentiles: 99%, 97.5%, 95%, 90%, 75%, 50%, 25% and >25%. To go back, simply refresh the page!

Network Scientists 2010

Words of Complex Networks

A corpus has been built from all arxiv abstracts concerning complex network and then, through word2vec, concepts have been retrieved. We can compute similarites and distances on these words, therefore we can embed words in space.

To visualise words and relations among them, we build an undirected weighted network (thresholding the cosine similarity matrix on the 98-percentile). In the following plot, the network structure reflects cosine similarity, node size depends on degree and node colour (brewer.pal("PuOr")) on betweenness centrality.

Words of Complex Networks - Degree and Betweenness

Since the this network is built upon a thresholded cosine similarity matrix, we work directly on the matrix (without thresholds) to get the embeding of this network of concepts into space.

Embedding in

Words of Complex Networks - Network Depth

The median concepts is dynamics, other central words are collective and behavior.


tags: demo; - network - depth;