The frequency and impacts of floods have increased in Iran in the past few decades. As flood events are in most instances associated with human activities, mitigating their impacts requires non-structural and community-based initiative alongside structural measures. In this connection, the concept of community resilience has been advanced in recent times, which attaches importance to interactio...

Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to...

We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidd...

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the struc...

Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically ex...

Even as social networks have become fashionable over the last few years, the emphasis has been on "artificial" social networks (like Orkut, where you state your links explicitly) over "natural" social networks (where social links are deducted from user actions, i.e., those underlying the blogosphere, globally or regionally). Blog readers and writers form communities, and there are several tools...

Networks consisting of vertices and edges connecting some pairs of vertices are powerful abstractions of relational data, hence have become very popular tools in many fields including sociology, biology and physics [1]. The characteristic of community structure in networks, i.e., networks are naturally divided into modules or communities, has attracted huge attention in the past decade which ca...

We study the structure of social networks of students by examining the graphs of Facebook “friendships” at five American universities at a single point in time. We investigate each single-institution network’s community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of sel...

J. F. Ghiglione, C. Palacios, J. C. Marty, G. Mével, C. Labrune, P. Conan, M. Pujo-Pay, N. Garcia, and M. Goutx CNRS, UMR7621, Laboratoire d’Océanographie Biologique de Banyuls, Avenue Fontaulé, BP44, 66650 Banyuls-sur-Mer, France UPMC Univ Paris 06, UMR 7621, Laboratoire ARAGO, Avenue Fontaulé, BP44, 66650 Banyuls-sur-Mer, France UMR 5244 CNRS UPVD EPHE, Biologie et Ecologie Tropicale et Médit...

INTERISLAND VARIABILITY OF DUNE PLANT COMMUNITY STRUCTURE ON VIRGINIA’S BARRIER ISLANDS Justin K. Shafer Old Dominion University, 2010 Director: Dr. Frank P. Day The barrier islands of the Virginia Coast Reserve LTER site provide an opportunity to study interisland variability of dune plant communities. My research focused on the variation in biomass and diversity of communities among islands a...