[Forum SIS] 9th Seminar D2 Seminar Series FDS - 10th November 3-4.30 pm

[datascience a unifi.it]

Dear all,

We are happy to present the ninth Seminar of the "D2 Seminar Series"
launched by the FDS. The Seminar will be held online FRIDAY 10TH OF
NOVEMBER 2021, from 3-4.30 PM.
The seminar will be held by Raffaele Guetto and Andrea Marino from the
Department of Statistics, Computer Science, Applications "G. Parenti" of
the University of Florence. 

Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA 

After registering, you will receive a confirmation email containing
information about joining the webinar.

We hope to see you there! You are invited to invite also your students,
PhDs and colleagues who may be interested in the Seminar (you find a
Flyer with all the info attached).

Kind Regards,
Florence Center for Data Science 

---------------- 
SPEAKER: Raffaele Guetto - Department of Statistics, Computer Science,
Applications "G. Parenti" -  University of Florence 
TITLE: Italy's lowest-low fertility in times of uncertainty 

ABSTRACT: The generalized and relatively homogeneous fertility decline
across European countries in the aftermath of the Great Recession poses
serious challenges to our knowledge of contemporary low fertility
patterns. The rise of economic uncertainty has often been identified, in
the sociological and demographic literature, as the main cause of this
state of affairs. The forces of uncertainty have been traditionally
operationalized through objective indicators of individuals' actual and
past labour market situations. However, this presentation argues that
the role of uncertainty needs to be conceptualized and operationalized
taking into account that people use works of imagination, producing
their own narrative of the future, also influenced by the media. To
outline such an approach, I review contemporary drivers of Italy's
lowest-low fertility, placing special emphasis on the role of
uncertainty fueled by labour market deregulations and - more recently -
the Covid-19 pandemic. I discuss the effects of the objective
(labour-market related) and subjective (individuals' perceptions,
including future outlooks) sides of uncertainty on fertility, based on a
set of recent empirical findings obtained through a variety of data and
methods. In doing so, I highlight the potential contribution of
so-called "big data" and techniques of media content analysis and
Natural Language Processing for the analysis of the effects of
media-conveyed narratives of the economy.

SPEAKER: Andrea Marino - Department of Statistics, Computer Science,
Applications "G. Parenti" - University of Florence 
TITLE: Approximating the Neighborhood Function of (Temporal) Graphs 

ABSTRACT: The average distance in graphs (like, for instance, the
Facebook friendship network and the Internet Movie Database
collaboration network), often referred to as degrees of separation, has
been largely investigated. However, if the number of nodes is very large
(millions or billions), computing this measure needs prohibitive time
and space costs as it requires to compute for each node the so-called
neighbourhood function, i.e. for each vertex v and for each h, how many
nodes are within distance h from v. Temporal graphs are a special kind
of graphs where edges have temporal labels, specifying their occurring
times, in the same way as the connections of the public transportation
network of a city are available only at scheduled times. Here, paths
make sense only if they correspond to sequences of edges with strictly
increasing labels. A possible notion of distance between two nodes in a
temporal network is the earliest arrival time of the temporal paths
connecting the two nodes. In this case, the temporal neighbourhood
function is defined as the number of nodes reachable from a given one in
a given time interval, and it is also expensive to compute. We introduce
probabilistic counting in order to approximate the size of sets and we
show how both plain and temporal neighbourhood functions can be
approximated by plugging this technique into a simple dynamic
programming algorithm.
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