Finding patterns of social interaction within a population has wide-ranging applications including: disease modeling, cultural and information transmission, and behavioral ecology. Social interactions are often modeled with networks. A key characteristic of social interactions is their continual change. However, most past analyses of social networks are essentially static in that all information about the time that social interactions take place is discarded. In this paper, we propose a new mathematical and computational framework that enables analysis of dynamic social networks and that explicitly makes use of information about when social interactions occur.

Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a “relational fixed-effect ” model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identifier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods. 1

This paper describes NetKit-SRL, or NetKit for short, a toolkit for learning from and classifying networked data. The toolkit is open-source and publicly available. It is modular and built for ease of plug-and-play---such that it is easy to add new modules and have them interact with other existing modules. Currently available NetKit modules are focused on "batch" within-network learning and classification: given a partially labeled network, where all nodes and edges are already known to exist, estimate the class membership probability of the unlabeled nodes in the network. NetKit has been used in various network domains such as websites, citation graphs, movies and social networks.

Abstract. Companies in nowaday’s business world have started to recognize the value of Social Network Analysis not only for the discovery of interhuman relations, but also for the examination of e.g. interorganizational dependencies and collaboration patterns. However, the corresponding IT-supported analyses require a large amount of explicitly structured data, which is rarely available and costly to create manually. The contribution of our presented approach to the above described problem is therefore two-fold. The first challenge is to bridge the gap between unstructered text data and the required network structures for the specific domain of business news. The aim is to obtain a network of companies that represents relevant facts (like industry relatedness) as close to reality as possible. We will then apply a chosen catalogue of social network analysis techniques in order to validate their suitability and to generate immediate feedback for the network generation step.

Text mining and machine learning methodologies have been applied to biomedicine and business domains for new relationship and knowledge discovery. Company annual reports (or 10K filings), as one of the most important mandatory information disclo-sures, have remained untapped by the text mining and machine learning community. Previous research indicates that the narrative disclosures in company annual reports can be used to assess the company’s short-term financial prospects. In this study, we apply text classification methods to 10K filings to systematically assess the pre-dictive potential of company annual reports. We specify our research problem along five dimensions: financial performance indicators, choice of predictions, evaluation criteria, document representation, and experiment design. Different combinations of the choices we made along the five dimensions provide us with different perspectives and insights into the feasibility of using annual reports to predict company future per-formance. Our results confirm that predictive models can be successfully built using the textual content of annual reports. Mock portfolios constructed with firms pre-dicted by the text-based model are shown to produce positive average stock return. Sub-sample experiments and post-hoc analysis further confirm that the text-based model is able to catch the textual differences among firms with different financial characteristics. We see a rich set of research questions with the promise of further insight in this research area.

Large volumes of online business news provide an opportunity to explore various aspects of companies. A news story pertaining to a company often cites other companies. Using such company citations we construct an intercompany network, employ social network analysis techniques to identify a set of attributes from the network structure, and feed the attributes to machine learning methods to predict the company revenue relation (CRR) that is based on two companies ’ relative quantitative financial data. Hence, we seek to understand the power of network structural attributes in predicting CRRs that are not described in the news or known at the time the news was published. The network attributes produce close to 80 % precision, recall, and accuracy for all 87,340 company pairs in the network. This approach is scalable and can be extended to private and foreign companies for which financial data is unavailable or hard to procure.

Activity recognition using mobile phones has great potential in many applications including mobile healthcare. In order to let a person easily know whether he is in strict compliance with the doctor’s exercise prescription and adjust his exercise amount accordingly, we can use a smart-phone based activity reporting system to accurately recognize a range of daily activities and report the duration of each activity. A triaxial accelerometer embedded in the smart phone is used for the classification of several activities, such as staying still, walking, running, and going upstairs and downstairs. The model learnt from a specific person often cannot yield accurate results when used on a different person. To solve the cross-people activity recognition problem, we propose an algorithm known as TransEMDT (Transfer learning EMbedded Decision Tree) that integrates a decision tree and the k-means clustering algorithm for personalized activity-recognition model adaptation. Tested on a real-world data set, the results show that our algorithm outperforms several traditional baseline algorithms.