Abstract-In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Q ds ) which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first of them, referred to as Fine-tuned Q, is based on modularity (Q) while the second one is based on Modularity Density (Q ds ) and denoted as Fine-tuned Q ds . Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy Q), Fine-tuned Q, and Fine-tuned Q ds on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Q ds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Q ds can be applied to the communities detected by other algorithms to significantly improve their results.
2Our quantitative understanding of how scientists choose and shift their research focus over time is highly consequential, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded [1][2][3][4][5][6][7][8][9]. Despite extensive investigations of various factors that influence a scientist's choice of research topics [8][9][10][11][12][13][14][15][16][17][18][19][20][21], quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, finding that research interest change follows a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research interest evolution [5,22]. We develop a random walk based model, allowing us to accurately reproduce the empirical observations. This work presents a quantitative analysis of macroscopic patterns governing research interest change, discovering a high degree of regularity underlying scientific research and individual careers."The essential tension" hypothesis set forth by Thomas Kuhn [5] has vividly highlighted the conflicting demands of scientific careers that require both exploration and exploitation [4,8,22].Indeed, career advancement, from promotion to obtaining grants, demands a steady stream of publications, which is often achieved through uninterrupted yet incremental contributions to existing, established research agenda. In contrast, frequent changes in research topics invite risk of failure and decreased productivity. The disciplinary boundaries, arising from such factors as implicit culture, tacit and accumulated knowledge [23,24] and peer recognition [3,25], together with intensifying specialization in science and engineering disciplines [26], make radical shifts, such as moving from chemical biology to high energy physics, extremely unlikely, if at all possible. On the other hand, although steady and focused research portfolio helps scientists stay productive, it potentially undermines chances for originality [8]. Indeed, innovative and novel insights often emerge from encountering new challenges and opportunities associated with venturing into new topics and/or incorporating them into existing research agenda [4,15,20,27,28].Given the broad impact on individual careers and strong implications for science and innovation policy, there is an urgent need for quantitative approaches to understanding the nature of research interest change undertaken by individual scientists throughout their careers. This becomes ever more so with the accelerating scale and complexity of scientific enterprise [2,26,29,30]. A variety of microscopic factors have been identified that drive a scientist's choice of research problems, 3 ranging from age...
A new network simulator, called SENSE, has been developed for simulating wireless sensor networks. The primary design goal is to address such factors as extensibility, reusability, and scalability, and to take into account the needs of different users. The recent progresses in component-based simulation, namely the component-port model and the simulation component classification, provided a sound theoretical foundation for the simulator. Practical issues, such as efficient memory usage, sensor network specific models, were also considered. Consequently, SENSE becomes an ease-of-use and efficient simulator for sensor network research.
Community structure 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 their structure and dynamics on networked systems. Here, in the first section, we review the work on generative models of communities and their role in developing strong foundation for community detection algorithms. We discuss modularity and algorithms based on modularity maximization. Then we follow with an overview of the Stochastic Block Model and its different variants as well as inference of communities structures from the model. The following section 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. We briefly discuss considerable advances made in this field recently. In the last section, we discuss 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.
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