Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page

Motif formation and emergence of mesoscopic structure in complex networks

PhDNetwork 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)

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp