and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the “best ” possible community—according to the conductance measure—over a wide range of size scales, and we study over 70 large sparse real-world networks taken from a wide range of application

of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the “best ” possible community—according to the conductance measure—over a wide range of size

sample of the members of the Facebook (a social network for colleges and high schools) at a US academic institution, and compare the survey data to information retrieved from the network itself. We look for underlying demographic or behavioral differences between the communities of the network’s members

Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today’s online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites

profile features beyond overall similarity that affect how much subjects trust a hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using

profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a webbased presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.

A Community Profile is a combined picture of the members data in the community and the process of creating it is called community profiling. It uses a variety of different techniques to build up a picture of the community from a number of different perspectives. The members in the Community can

Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social

intensity represent the development of efficient rill network while, no rill was found with a slope of 20 ˚ and a rainfall intensity of 60 mm·h−1. The highest rill length was observed in plot E with 20 ˚ slope and 120 mm·h−1 rainfall intensity at 360 minutes. Positive and strong correlation (R2 = 0.734, P

based community detection in social networks