Resilience Engineering has evolved during the recent century and could be a good complement to the prevailing ideas concerning safety within the air traffic industry. The concept of Resilience Engineering stresses the fact that in order to keep up the high standard of safety, there must be greater attention directed to the importance of being proactive, and to implement measures before dangerous situations arises. The purpose of our work was to develop the Resilience Analysis Grid (RAG) to help LFV, the leading Air Navigation Service Provider in Sweden, to identify their ability to deal with disturbances and unexpected events. By testing our RAG on seven active air traffic controllers and operational managers, we were able to produce a final set of assertions, with a total number of 22 items, which LFV (or other similar organisations) can use as a foundation for future RAG studies. As a first attempt we also rated the answers which gave us an opportunity to produce a star diagram, showing the relationship between the areas covered by the RAG. During the interviews we discovered that resilience is already today in many aspects a big part of the everyday work and that the RAG method can therefore be applicable in the industry with some modification. However, there are certain areas within LFV that we believe there is room for improvements. We believe that the RAG could serve as a helpful tool in identifying these areas as well as assisting LFV in their striving to remain one of the safest organisations in the world.

Resilience Engineering has evolved during the recent century and could be a good complement to the prevailing ideas concerning safety within the air traffic industry. The concept of Resilience Engineering stresses the fact that in order to keep up the high standard of safety, there must be greater attention directed to the importance of being proactive, and to implement measures before dangerous situations arises. The purpose of our work was to develop the Resilience Analysis Grid (RAG) to help LFV, the leading Air Navigation Service Provider in Sweden, to identify their ability to deal with disturbances and unexpected events. By testing our RAG on seven active air traffic controllers and operational managers, we were able to produce a final set of assertions, with a total number of 22 items, which LFV (or other similar organisations) can use as a foundation for future RAG studies. As a first attempt we also rated the answers which gave us an opportunity to produce a star diagram, showing the relationship between the areas covered by the RAG. During the interviews we discovered that resilience is already today in many aspects a big part of the everyday work and that the RAG method can therefore be applicable in the industry with some modification. However, there are certain areas within LFV that we believe there is room for improvements. We believe that the RAG could serve as a helpful tool in identifying these areas as well as assisting LFV in their striving to remain one of the safest organisations in the world.

The past decades have seen a fast-growing and dynamic trend of network science and its applications. From the Internet to Facebook, from telecommunications to power grids, from protein interactions to paper citations, networks are everywhere and the network paradigm is pervasive. Network analysis and mining has become an important tool for scientific research and industrial applications to diverse domains. For example, finding communities within social networks enables us to identify groups of densely connected customers who may share similar interests and behaviors and thus generate more effective recommender systems; investigating the supply-network topological structure and growth model improves the resilience of supply networks against disruptions; and modeling influence diffusion in social networks provides insights into viral marketing strategies. However, none of these tasks is trivial. In fact, community detection, resilience analysis, and influence-diffusion modeling are all important challenges in complex networks. My PhD research contributes to these endeavors by exploring the implicit knowledge of connectivity and proximity encoded in the network graph topology. Our research originated from an attempt to find communities in networks. After carefully examining real-life communities and the features and limitations of a set of widely-used centrality measures, we develop a simple but powerful reachability-based influence-diffusion model. Based upon this model, we propose a new influence centrality and a novel shared-influence-neighbor (SIN) similarity. The former differentiates the comprehensive influence significance more precisely, and the latter gives rise to a refined vertex-pair closeness metric. Then we develop an influence-guided spherical K-means (IGSK) algorithm for community detection. Further, we propose two novel influence-guided label propagation (IGLP) algorithms for finding hierarchical communities in complex networks. Experiments on both real-life networks and synthetic benchmarks demonstrate superior performance of our algorithms in both undirected/directed and unweighted/weighted networks. Another research topic we investigated is resilience analysis of supply networks. Supply networks play an important role in product distribution, and survivability is a critical concern in supply-network design and analysis. We exploit the resilience embedded in supply-network topology by exploring the multiple-path reachability of each demand node to other nodes, and propose a novel resilience metric. We also develop new supply-network growth mechanisms that reflect the heterogeneous roles of different types of units in supply networks. We incorporate them into two fundamental network topologies (random-graph topology and scale-free topology), and evaluate the resilience under random disruptions and targeted attacks using the new resilience metric. The experimental results verify the validity of our resilience metric and the effectiveness of our growth model. This research provides a generic framework and important insights into the construction of robust supply networks. Finally, we investigate activation-based influence-diffusion modeling for viral marketing. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the largest further adoptions through word-of-mouth-based influence propagation in the network. We propose a novel multiple-path asynchronous threshold (MAT) model, in which we quantitatively measure influence and keep track of its diffusion and aggregation during the diffusion process. Our MAT model captures both direct and indirect influence, influence attenuation along diffusion paths, temporal influence decay, and individual diffusion dynamics. Our work is an important step toward a more realistic diffusion model. Further, we develop two effective and efficient heuristics (IV-Greedy and IV-Community) to tackle the influence-maximization problem. Our experiments on four real-life networks demonstrate their excellent performance in terms of both influence spread and efficiency. Our work provides preliminary but significant insights and implications for diffusion research and marketing practice.

In future cities, urban drainage and flood management systems should be designed not only to reliable during normal operating conditions but also to be resilient to exceptional threats that lead to catastrophic failure impacts and consequences. Resilience can potentially be built into urban drainage systems by implementing a range of strategies, for example by embedding redundancy and flexibility in system design or rehabilitation to increase their ability to efficiently maintain acceptable customer flood protection service levels during and after occurrence of failure or through installation of equipment that enhances customer preparedness for extreme events or service disruptions. However, operationalisation of resilience in urban flood management is still constrained by lack of suitable quantitative evaluation methods. Existing hydraulic reliability-based approaches tend to focus on quantifying functional failure caused by extreme rainfall or increases in dry weather flows that lead to hydraulic overloading of the system. Such approaches take a narrow view of functional resilience and fail to explore the full system failure scenario space due to exclusion of internal system failures such as equipment malfunction, sewer (link) collapse and blockage that also contribute significantly to urban flooding. In this research, a new analytical approach based on Global Resilience Analysis (GRA) is investigated and applied to systematically evaluate the performance of an urban drainage system (UDS) when subjected to a wide range of both functional and structural failure scenarios resulting from extreme rainfall and pseudo random cumulative link failure respectively. Failure envelopes, which represent the resulting loss of system functionality (impacts) are determined by computing the upper and lower limits of the simulation results for total flood volume (failure magnitude) and average flood duration (failure duration) at each considered failure level. A new resilience index is developed and applied to link resulting loss of functionality magnitude and duration to system residual functionality (head room) at each considered failure level. With this approach, resilience has been tested and characterized for a synthetic UDS and for an existing UDS in Kampala city, Uganda. In addition, the approach has been applied to quantify the impact of interventions (adaptation strategies) on enhancement of global UDS resilience to flooding. The developed GRA method provides a systematic and computationally efficient approach that enables evaluation of whole system resilience, where resilience concerns ‘beyond failure’ magnitude and duration, without prior knowledge of threat occurrence probabilities. The study results obtained by applying the developed method to the case studies suggest that by embedding the cost of failure in resilience-based evaluation, adaptation strategies which enhance system flexibility properties such as distributed storage and improved asset management are more cost-effective over the service life of UDSs.