Resilience of the Critical Communication Networks Against Spreading Failures: Case of the European National and Research Networks

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed

Detecing Anti-Vaccine Users on Twitter

Vaccine hesitancy, which has recently been driven by online narratives, significantly degrades the efficacy of vaccination strategies, such as those for COVID-19. Despite broad agreement in the medical community about the safety and efficacy of available vaccines, a large number of social media users continue to be inundated with false information about vaccines and are indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narratives. We introduce a publicly available Python package capable of analyzing Twitter profiles to assess how likely that profile is to share anti-vaccine sentiment in the future. The software package is built using text embedding methods, neural networks, and automated dataset generation and is trained on several million tweets. We find this model can accurately detect anti-vaccine users up to a year before they tweet anti-vaccine hashtags or keywords. We also show examples of how text analysis helps us understand anti-vaccine discussions by detecting moral and emotional differences between anti-vaccine spreaders on Twitter and regular users. Our results will help researchers and policy-makers understand how users become anti-vaccine and what they discuss on Twitter. Policy-makers can utilize this information for better targeted campaigns that debunk harmful anti-vaccination myths

What are Your Pronouns? Examining Gender Pronoun Usage on Twitter

Stating your gender pronouns, along with your name, is becoming the new norm of self-introductions at school, at the workplace, and online. The increasing prevalence and awareness of nonconforming gender identities put discussions of developing gender-inclusive language at the forefront. This work presents the first empirical research on gender pronoun usage on large-scale social media. Leveraging a Twitter dataset of over 2 billion tweets collected continuously over two years, we find that the public declaration of gender pronouns is on the rise, with most people declaring as using she series pronouns, followed by he series pronouns, and a smaller but considerable amount of non-binary pronouns. From analyzing Twitter posts and sharing activities, we can discern users who use gender pronouns from those who do not and also distinguish users of various gender identities. We further illustrate the relationship between explicit forms of social network exposure to gender pronouns and their eventual gender pronoun adoption. This work carries crucial implications for gender-identity studies and initiates new research directions in gender-related fairness and inclusion, as well as support against online harassment and discrimination on social media.Comment: 23 pages, 11 figures, 2 table

Resilience of the Critical Communication Networks Against Spreading Failures: Case of the European National and Research Networks

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed

Resilience of the Critical Communication Networks Against Spreading Failures: Case of the European National and Research Networks

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed

Using LTI Dynamics to Identify the Influential Nodes in a Network.

Networks are used for modeling numerous technical, social or biological systems. In order to better understand the system dynamics, it is a matter of great interest to identify the most important nodes within the network. For a large set of problems, whether it is the optimal use of available resources, spreading information efficiently or even protection from malicious attacks, the most important node is the most influential spreader, the one that is capable of propagating information in the shortest time to a large portion of the network. Here we propose the Node Imposed Response (NiR), a measure which accurately evaluates node spreading power. It outperforms betweenness, degree, k-shell and h-index centrality in many cases and shows the similar accuracy to dynamics-sensitive centrality. We utilize the system-theoretic approach considering the network as a Linear Time-Invariant system. By observing the system response we can quantify the importance of each node. In addition, our study provides a robust tool set for various protective strategies

The <i>NiR</i> of all nodes in the network.

<p>For each node, we can calculate the <i>NiR</i>. Initially, the undirected network is made directed and acyclic with the respect to the source node. This is a necessary step in order to maintain the system’s stability. The LTI system is then formulated having in mind the new topology and the source. The value of the maximum step response of the corresponding system is the <i>NiR</i>. There are two topologies depicted for two observed nodes: node 1 (left) and node 10 (right). The normalized <i>NiR</i> values of all nodes are shown. The radius of the node represents the same (larger the radius, larger the <i>NiR</i>. Here we can identify the node 5 as the one with highest <i>NiR</i>.</p

Generated and extracted networks.

<p>Four networks are generated using Barabási-Albert and Watts-Strogatz models for <i>scale-free</i> and <i>small-world</i> networks respectively. The rest are the real world networks of various sizes and characteristics taken from: <i>SNAP—Stanford Large Network Dataset Collection</i>, <i>UCLA’s Beyond BGP:Internet Topology Project</i> and <i>The Internet Topology Zoo</i>. All data sets are available online. Column <i>nodes</i> represents the number of nodes in the original network. Columns <i>diameter</i>, <i>density</i> and <i>clust. coeff.</i> represent the mean values calculated from the set of sampled networks. The <i>avg. degree</i> is the same for both the original and sampled networks.</p

Expected time of infection and step response: small networks example.

<p>For three small networks the expected time of full infection, <i>E</i> [<i>X</i>(<i>p</i>)], is calculated. For all networks the source of the infection is the parent node (top). At each time step the parent node tries to infect neighboring susceptible nodes with the probability <i>p</i>. All nodes will be eventually infected and the time of full infection is presented with a certain distribution (i.e. the distribution of the expected number of trials in discrete time for the infection to reach all nodes). The <i>E</i> [<i>X</i>(<i>p</i>)] is the mean of the distribution for each network (the expected number of trials before the success.) The <i>S</i>_<i>max</i>(<i>p</i>) is the maximum step response value of the corresponding LTI system.</p

The correlation between degree and the <i>NiR</i>.

<p>There is a strong correlation between degree and <i>NiR</i> value for all types of the networks observed. The correlation against the node degree for all networks takes a value of 0.94 ± 0.02. The sum of all degrees of the node’s neighbors (degree of the distance one) correlates even more with the <i>NiR</i> value when the correlation coefficient is 0.98 ± 0.01. Random scale-free network, as well as the General Relativity collaboration network (<i>ca-GrQc</i>) show the expected pattern on the graph clearly demonstrating the presence of the small number of hubs, compared to the relatively large number of non-central nodes. On the other hand, the small-world model generates approximately the same number of nodes which could be grouped by the importance.</p