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Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.

Remote identification of people is an important capability for security systems. Automatically controlling a pan-tiltzoom camera is an effective way to collect high resolution video or images of people in an unconstrained environment. Often there will be more people in an area than cameras available. The cameras must then divide their time among the people in order to view everyone. In this paper, we discuss the challenges involved in scheduling an active camera to observe multiple people. We present some candidate scheduling policies to address these challenges and evaluate their performance. The evaluation was conducted with a simulation based on data collected with our cooperative active camera system.

Abstract-Spike sorting of neural data from single electrode recordings is a hard problem in machine learning that relies on significant input by human experts. We approach the task of learning to detect and classify spike waveforms in additive noise using two stages of large margin kernel classification and probability regression. Controlled numerical experiments using spike and noise data extracted from neural recordings indicate significant improvements in detection and classification accuracy over amplitude-and linear template-based spike sorting techniques.