CSS/Italy

Italian Chapter of Complex Systems Society

research

research

The multiplex network of human diseases

Arda Halu, Manlio De Domenico, Alex Arenas and Amitabh Sharma
npj Systems Biology and Applications 5, 15 (2019)
DOI: 10.1038/s41540-019-0092-5

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

research

The fragility of decentralised trustless socio-technical systems

Manlio De Domenico and Andrea Baronchelli
EPJ Data Science 8, 2 (2019)
DOI: 10.1140/epjds/s13688-018-0180-6

The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of “emergent centralisation” to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world.

research

Bots increase exposure to negative and inflammatory content in online social systems

Massimo Stella, Emilio Ferrara, and Manlio De Domenico
PNAS 115, 12435 (2018)
doi: 10.1073/pnas.1803470115

Societies are complex systems, which tend to polarize into subgroups of individuals with dramatically opposite perspectives. This phenomenon is reflected—and often amplified—in online social networks, where, however, humans are no longer the only players and coexist alongside with social bots—that is, software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives, and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.

 

research

Optimal positioning of storage systems in microgrids based on complex networks centrality measures

By Saman Korjani, Angelo Facchini, Mario Mureddu, Guido Caldarelli, Alfonso Damiano

Published in Scientific Reports (2018) 8:16658

 

We propose a criterion based on complex networks centrality metrics to identify the optimal position of Energy Storage Systems in power networks. To this aim we study the relation between centrality metrics and voltage fluctuations in power grids in presence of high penetration of renewable energy sources and storage systems. For testing purposes we consider two prototypical IEEE networks and we compute the correlation between node centrality (namely Eigenvector, Closeness, Pagerank, Betweenness) and voltage fluctuations in presence of intermittent renewable energy generators and intermittent loads measured from domestic users. We show that the topological characteristics of the power networks are able to identify the optimal positioning of active and reactive power compensators (such as energy storage systems) used to reduce voltage fluctuations according to the common quality of service standards. Results show that, among the different metrics, eigenvector centrality shows a statistically significant exponential correlation with the reduction of voltage fluctuations. This finding confirms the technical know-how for which storage systems are heuristically positioned far from supply reactive nodes. This also represents an advantage both in terms of computational time, and in terms of planning of wide resilient networks, where a careful positioning of storage systems is needed, especially in a scenario of interconnected microgrids where intermittent distributed energy sources (such as wind or solar) are fully deployed.