Correction: Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

[This corrects the article DOI: 10.1371/journal.pone.0258666.].

Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

To understand airline transportation networks (ATN) systems we can effectively represent them as multilayer networks, where layers capture different airline companies, the nodes correspond to the airports and the edges to the routes between the airports. We focus our study on the importance of leveraging synthetic generative multilayer models to support the analysis of meaningful patterns in these routes, capturing an ATN's evolution with an emphasis on measuring its resilience to random or targeted attacks and considering deliberate locations of airports. By resorting to the European ATN and the United States ATN as exemplary references, in this work, we provide a systematic analysis of major existing synthetic generation models for ATNs, specifically ANGEL, STARGEN and BINBALL. Besides a thorough study of the topological aspects of the ATNs created by the three models, our major contribution lays on an unprecedented investigation of their spectral characteristics based on Random Matrix Theory and on their resilience analysis based on both site and bond percolation approaches. Results have shown that ANGEL outperforms STARGEN and BINBALL to better capture the complexity of real-world ATNs by featuring the unique properties of building a multiplex ATN layer by layer and of replicating layers with point-to-point structures alongside hub-spoke formations.

Twitter Response to Munich July 2016 Attack: Network Analysis of Influence.

Social Media platforms in Cyberspace provide communication channels for individuals, businesses, as well as state and non-state actors (i.e., individuals and groups) to conduct messaging campaigns. What are the spheres of influence that arose around the keyword #Munich on Twitter following an active shooter event at a Munich shopping mall in July 2016? To answer that question in this work, we capture tweets utilizing #Munich beginning 1 h after the shooting was reported, and the data collection ends approximately 1 month later. We construct both daily networks and a cumulative network from this data. We analyze community evolution using the standard Louvain algorithm, and how the communities change over time to study how they both encourage and discourage the effectiveness of an information messaging campaign. We conclude that the large communities observed in the early stage of the data disappear from the #Munich conversation within 7 days. The politically charged nature of many of these communities suggests their activity is migrated to other Twitter hashtags (i.e., conversation topics). Future analysis of Twitter activity might focus on tracking communities across topics and time.

Identifying network structure similarity using spectral graph theory.

Most real networks are too large or they are not available for real time analysis. Therefore, in practice, decisions are made based on partial information about the ground truth network. It is of great interest to have metrics to determine if an inferred network (the partial information network) is similar to the ground truth. In this paper we develop a test for similarity between the inferred and the true network. Our research utilizes a network visualization tool, which systematically discovers a network, producing a sequence of snapshots of the network. We introduce and test our metric on the consecutive snapshots of a network, and against the ground truth. To test the scalability of our metric we use a random matrix theory approach while discovering Erdös-Rényi graphs. This scaling analysis allows us to make predictions about the performance of the discovery process.