The paper titled "On community structure in complex networks: challenges and opportunities," by Hocine Cheriffi, Gergely Palla, Boleslaw K. Szymanski and Xiaoyan Lu has been published in Applied Network Science today

The paper titled "On community structure in complex networks: challenges and opportunities," by Hocine Cherifi, Gergely Palla, Boleslaw K. Szymanski, and Xiaoyan Lu has been published in Applied Network Science vol. 4:117 (2017). The paper focuses on community structure that is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. The paper first focuses on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. Modularity and its maximization use as the basis for community detection are discussed. Then, the paper overviews the Stochastic Block Model and its different variants as well as the inference of community structures from such models. Next, the paper focuses on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. The paper briefly discusses considerable advances made in this field recently. Finally, the paper focuses on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network. The paper is available under link below.