Abstract Community detection is one of the most important problems in complex network research. In recent years, great efforts have been devoted to this problem in term of evaluating the resulting community structure. Our previous work has shown that in addition to the resolution limit of Q, both Q and D suffer from a more serious limitation, termed as extra weak community phenomenon, i.e. some...

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this work Artificial Fish Swarm op...

The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer...

The Internet is considered a complex network for its size, interconnectivity and rules that govern are dynamic, because of constantly evolve. For this reason the search of distributed resources shared by users and online communities is a complex task that needs efficient search method. The goal of this work is to improve the performance of distributed search of information, through analysis of ...

Community detection for uncovering the hidden community structure in large networks is an important task in analyzing complex networks. Most of the existing methods only consider link structure in networks, where the link information is usually sparse and noisy, which may result in a poor partition of a network. Fortunately, besides link structure, nodes, especially in social networks, are ofte...

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so...

Environmental and public health policy continues to evolve in response to new and complex social, economic and environmental drivers. Globalization and centralization of commerce, evolving patterns of land use (e.g., urbanization, deforestation), and technological advances in such areas as manufacturing and development of genetically modified foods have created new and complex classes of stress...

Community corrections centers (CCCs) are known to be an effective criminal justice strategy. However, location of CCCs is challenging: residents of potential destination communities often regard them as “undesirable” land uses. We develop a methodology by which a small set of feasible CCC locations may be identified from a larger initial set using data on neighborhood characteristics and curren...