Emilio Ferrara, PhD

E. Ferrara, P. De Meo, G. Fiumara, and A. Provetti.
CHASM ’12: Computational Approaches to Social Modeling.

In this paper we report our findings on the analysis of two large datasets representing the friendship structure of the well-known Facebook network.
In particular, we discuss the quantitative assessment of the strength of weak ties Granovetter’s theory, considering the problem from the perspective of the community
structure of the network.

We describe our findings providing some clues of the validity of this theory also for a large-scale online social network such as Facebook.

In detail, we characterized their overall statistical distribution, as a function of the size of the communities and the density of weak ties among communities.

Download Facebook datasets !

Useful links: PDF | BibTex | Arxiv

E. Ferrara
International Journal of Social Network Mining, 1(1):67–90 (2012).

In this paper I presented the analysis of the community structure of Facebook.

Data have been collected directly from the Facebook social network, adopting two different sampling techniques (i.e., uniform sampling and breadth-first search sampling).

Once obtained the datasets, I unveiled the community structure of Facebook by adopting two computationally efficient algorithms, respectively, “Label Propagation Algorithm” (LPA) and “Fast Network Community Algorithm” (FNCA), well-suited for large scale community detection tasks.

Results have been compared in order to evaluate the bias introduced both by the sampling and the clustering processes, assessing the validity of obtained analysis.

The main findings of this works can be summarized as follows:

• The distribution of the size of the communities on a large scale real-world online social networks such as Facebook follows a power law.
• The community structure of Facebook is well-defined: the results obtained by using different sampling methods and different community detection algorithms are hightly overlapping and share a high degree of similarity.
• Algorithms based on network modularity optimization (such as FNCA) introduce some bias in the community detection due to the well-known resolution limit.
• The uniform sampling produces an unbiased sample of a large scale network and well-reflects the so-called community structure.

Dowload Facebook Datasets !

Useful links: PDF | BibTex | Journal page

P. De Meo, E. Ferrara, G. Fiumara and A. Ricciardello.
Knowledge-based Systems 30:136-150 (2012).

In this paper we presented the K-path edge centrality, a novel measure of edge centrality for social networks based on the paradigm of information diffusion.

In addition, we provide a near-linear time algorithm (called WERW-Kpath) to compute this centrality index and we provide a theoretical proof of the approximation of the error introduced by this algorithm.

The strength of the K-path edge centrality is that it can be adopted to weight unweighted networks, assigning to each edge a weight proportional to its tendency to spread information over the networks.

The rationale behind the K-path edge centrality is rooted in random walks theory: in particular, WERW-Kpath simulates a fixed number of simple (i.e, acyclic) random paths of bounded length up to K (called K-paths) and, at each iteration, increases the weight of each edge composing the given K-path.

Download WERW-Kpath !

Useful links: PDF | BibTex | Journal page

S. Catanese, E. Ferrara, and G. Fiumara.
Social Network Analysis and Mining (2012).

In this paper we presented LogAnalysis, a Social Network Analysis tool designed to manage with communication networks (e.g., phone call networks, mobile phone networks).

In detail, it provides visual data representation of networks, statistical analysis and temporal evolution analysis of communication activities.

In the paper it is also discussed the usage of LogAnalysis during real forensic investigations as a tool to understand communication dynamics in criminal networks, discovering communities of suspected individuals, unveiling hierarchies and structure of criminal rings and much more.

Useful links: PDF | BibTex | Journal page