The interdisciplinary field of network science has attracted enormous attention in the past 10 years, although most results have been obtained by analyzing isolated networks. However many real-world networks interact with and depend on other networks.
"The images on this page can be used to broadly understand the general structure of our professional network. For example, we see strong country clusters but we also see many strong connections between clusters. This will not be surprising to anyone in the Pacific—oftentimes our first conversation with a stranger is ‘who do we both know?’ to establish mutual friends and colleagues. In the first network map below, you can see broad trends of connection and centrality within the network. If you download the high resolution version of the second map below, you will be able to see individuals’ names and their connections to others. Exploring these maps, you can begin to look for connections that your friends and colleagues have. When considering future collaborations, you can explore who knows who within the region." ...
In the August issue of Nature Biotechnology, a team led by Broad associate member Manolis Kellis introduced a new algorithm that may make this work easier for network researchers in a variety of fields. The team developed a method called “network deconvolution” that helps researchers identify the most direct connections within complex networks by filtering out the background noise caused by indirect or incidental connections.
The Lincoln Laboratory Journal showcases some of the Laboratory's most innovative and high-impact work, in fields ranging from air traffic control to bioagent sensing to parallel computing. The Journal consists of in-depth feature articles written by Laboratory staff members as well as shorter "Lab Notes" written by the Journal editors.
Lamia Ben's insight:
MIT's Lincoln Laboratory Journal's special issue focuses on graphs and networks.
Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be. DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702
Origin of Peer Influence in Social Networks Phys. Rev. Lett. 112, 098702 – Published 6 March 2014 Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco
"Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering."
Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality.
Race and poverty are not as important as a person’s social network in predicting whether he or she will become a victim of a fatal shooting in Chicago, Yale University sociologists found in a study released Thursday.
If A Network Is Broken, Break It More by Sophie Bushwick, Inside Science From the World Wide Web to the electrical grid, networks are notoriously difficult to control. A disturbance to just one part...
Lamia Ben's insight:
New research suggests that by selectively damaging part of a broken network, we can bring the entire system to a better state.