This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a stateof-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality.For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system CONFERATOR.

No abstract

Today, many people spend a lot of time online. Their social interactions captured in online social networks are an important part of the overall personal social profile, in addition to interactions taking place offline. This paper investigates whether relations captured by online social networks can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity and coauthorship networks. Particularly, the paper focuses on interactions of computer scientists in online settings (homepages, social networks profiles and connections) and offline settings (scientific collaboration, faceto-face communications during the conferences). We focus on quantitative studies and investigate the structural similarities and correlations of the induced networks; in addition, we analyze implications between networks. Finally, we provide a qualitative user analysis to find characteristics of good and bad proxies.

Finding the right features and patterns for identifying relations in natural language is one of the most pressing research questions for relation extraction. In this paper, we compare patterns based on supervised and unsupervised syntactic parsing and present a simple method for extracting surface patterns from a parsed training set. Results show that the use of surfacebased patterns not only increases extraction speed, but also improves the quality of the extracted relations. We find that, in this setting, unsupervised parsing, besides requiring less resources, compares favorably in terms of extraction quality.