Motif formation and emergence of mesoscopic structure in complex networks
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Network structures can encode information from datasets that have a natural representation
in terms of networks, for example datasets describing collaborations or social
relations among individuals in science or society, as well as from data that can be mapped
into graphs due to their intrinsic correlations, such as time series or images. Developing
models and algorithms to characterise the structure of complex networks at the micro
and mesoscale is thus of fundamental importance to extract relevant information from
and to understand real world complex data and systems. In this thesis we will investigate
how modularity, a mesoscopic feature observed almost universally in real world
complex networks can emerge, and how this phenomenon is related to the appearance of
a particular type of network motif, the triad. We will shed light on the role that motifs
play in shaping the mesoscale structure of complex networks by considering two special
classes of networks, multiplex networks, that describe complex systems where interactions
of different nature are involved, and visibility graphs, a family of graphs that can
be extracted from the time series of dynamical processes. This thesis is based on the
research papers listed below, in particular on the first five, published between 2014 and
2016:
1. Bianconi, G., Darst R. K., Iacovacci J., Fortunato S., Triadic closure as a basic generating
mechanism of communities in complex networks, Phys. Rev. E 90 (4), 042806
(2014).
2. Iacovacci J., Wu Z., Bianconi G., Mesoscopic structures reveal the network between
the layers of multiplex data sets, Phys. Rev. E. 92 (4), 042806 (2015).
3. Battiston F., Iacovacci J., Nicosia V., Bianconi G., Latora V., Emergence of multiplex
communities in collaboration networks, PloS one 11 (1), e0147451 (2016).
4. Iacovacci J., Lacasa L., Sequential visibility-graph motifs, Phys. Rev. E. 93 (4),
042309 (2016).
5. Iacovacci J., Lacasa L., Sequential motif pro le of natural visibility-graphs, Phys.
Rev. E. 94 (5), 052309 (2016).
6. Iacovacci J., Bianconi G., Extracting information from multiplex networks, Chaos:
An Interdisciplinary Journal of Nonlinear Science 26 (6), 065306 (2016).
7. Iacovacci J., Rahmede C., Arenas A., Bianconi G., Functional Multiplex PageRank,
EPL (Europhysics Letters) 116(2), 28004 (2016).
8. Lacasa L, Iacovacci J., Visibility graphs of random scalar elds and spatial data,
arXiv preprint arXiv:1702.07813 (2017).
9. Rahmede C, Iacovacci J, Arenas A, Bianconi G., Centralities of Nodes and In
infuences of Layers in Large Multiplex Network, arXiv preprint arXiv:1703.05833 (2017).
Authors
Iacovacci, JacopoCollections
- Theses [4122]