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1. What network motifs tell us about resilience and reliability of complex networks

   https://doi.org/10.1073/pnas.1819529116  

   Dey, Asim K.; Gel, Yulia R.; Poor, H. Vincent (September 2019, Proceedings of the National Academy of Sciences)

   Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures. 

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2. GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

   https://doi.org/10.1093/bioinformatics/btaa459  

   Devkota, Kapil; Murphy, James M; Cowen, Lenore J (July 2020, Bioinformatics)

   Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. From Power to Water: Dissecting SCADA Networks Across Different Critical Infrastructures

   Ortiz, Neil; Rosso, Martin; Zambon, Emmanuele; den_Hartog, Jerry; Cardenas, Alvaro A (March 2024, Passive and Active Measurement)

   In recent years, there has been an increasing need to understand the SCADA networks that oversee our essential infrastructures. While previous studies have focused on networks in a single sector, few have taken a comparative approach across multiple critical infrastructures. This paper dissects operational SCADA networks of three essential services: power grids, gas distribution, and water treatment systems. Our analysis reveals some distinct and shared behaviors of these networks, shedding light on their operation and network configuration. Our findings challenge some of the previous perceptions about the uniformity of SCADA networks and emphasize the need for specialized approaches tailored to each critical infrastructure. With this research, we pave the way for better network characterization for cybersecurity measures and more robust designs in intrusion detection systems. 

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4. DomiRank Centrality reveals structural fragility of complex networks via node dominance

   https://doi.org/10.1038/s41467-023-44257-0  

   Engsig, Marcus; Tejedor, Alejandro; Moreno, Yamir; Foufoula-Georgiou, Efi; Kasmi, Chaouki (December 2024, Nature Communications)

   Abstract Determining the key elements of interconnected infrastructure and complex systems is paramount to ensure system functionality and integrity. This work quantifies the dominance of the networks’ nodes in their respective neighborhoods, introducing a centrality metric, DomiRank, that integrates local and global topological information via a tunable parameter. We present an analytical formula and an efficient parallelizable algorithm for DomiRank centrality, making it applicable to massive networks. From the networks’ structure and function perspective, nodes with high values of DomiRank highlight fragile neighborhoods whose integrity and functionality are highly dependent on those dominant nodes. Underscoring this relation between dominance and fragility, we show that DomiRank systematically outperforms other centrality metrics in generating targeted attacks that effectively compromise network structure and disrupt its functionality for synthetic and real-world topologies. Moreover, we show that DomiRank-based attacks inflict more enduring damage in the network, hindering its ability to rebound and, thus, impairing system resilience. DomiRank centrality capitalizes on the competition mechanism embedded in its definition to expose the fragility of networks, paving the way to design strategies to mitigate vulnerability and enhance the resilience of critical infrastructures. 

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5. Topological clustering of multilayer networks

   https://doi.org/10.1073/pnas.2019994118  

   Yuvaraj, Monisha; Dey, Asim K.; Lyubchich, Vyacheslav; Gel, Yulia R.; Poor, H. Vincent (May 2021, Proceedings of the National Academy of Sciences)

   Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics. 

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