A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and the membership of groups often evolve gradually. In a dynamic multi-mode network, both actor membership and interactions can evolve, which poses a challenging problem of identifying community evolution. In this work, we try to address this issue by employing the temporal information to analyze a multi-mode network. A spectral framework and its scalability issue are carefully studied. Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships.
Abstract. Identifying patterns of factors associated with aircraft accidents is of high interest to the aviation safety community. However, accident data is not large enough to allow a significant discovery of repeating patterns of the factors. We applied the STUCCO 1 algorithm to analyze aircraft accident data in contrast to the aircraft incident data in major aviation safety databases and identified factors that are significantly associated with the accidents. The data pertains to accidents and incidents involving commercial flights within the United States. The NTSB accident database was analyzed against four incident databases and the results were compared. We ranked the findings by the Factor Support Ratio, a measure introduced in this work.
Over 2 million serious side effects, including 100,000 deaths, occur due to adverse drug reactions (ADR) every year in the US. Though various NGOs monitor ADRs through self reporting systems, earlier detection can be achieved using patient electronic health record (EHR) data available at many medical facilities. This paper presents an algorithm which allow existing ADR detection methods, which were developed for spontaneous reporting systems, to be applied directly to the longitudinal EHR data, as well as a new ADR detection method specifically for this type of data. Preliminary results show that the new method outperforms existing methods on EHR datasets. Future work on the method will extend it to detecting potential causeeffect relationships between events in other types of longitudinal data, handling multiple cause and effect items, and automatically selecting surveillance windows.
The goal of data analysis in aviation safety is simple: improve safety. However, the path to this goal is hard to identify. What data mining methods are most applicable to this task? What data are available and how should they be analyzed? How do we focus on the most interesting results? Our answers to these questions are based on a recent research project we completed. The encouraging news is that we found a number of aviation safety offices doing commendable work to collect and analyze safety-related data. But we also found a number of areas where data mining techniques could provide new tools that either perform analyses that were not considered before, or that can now be done more easily.
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