We have designed and implemented Friendlee, a mobile social networking application for close relationships. Friendlee analyzes the user's call and messaging activity to form an intimate network of the user's closest social contacts while providing ambient awareness of the user' social network in a compelling, yet non-intrusive manner.

In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected edges in a subgraph.This measure is adapted from spatial scan statistics for point sets and provides quantitative assessment for clusters.We discuss some important properties of this statistic and its relation to modularity and Bregman divergences.We apply a simple clustering algorithm to find clusters with large values of this measure in a variety of real-world data sets, and we illustrate its ability to identify statistically significant clusters of selected granularity.

Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose.

Abstract The ability to monitor the progress of a program in execution is useful in program testing and debugging, and for performance analysis and profiling. Systems to support this activity should ideally be high‐level‐language based, but progress in implementing them has been limited by the lack of suitable hardware support. In this paper we derive software structures to support a generalized system of execution monitoring, and define a set of architectural primitives sufficient to support these structures. Simple and efficient means of implementing these primitives within the machine architecture are described.