In this paper we discuss a very simple approach of combining content and link information in graph structures for the purpose of community discovery, a fundamental task in network analysis. Our approach hinges on the basic intuition that many networks contain noise in the link structure and that content information can help strengthen the community signal. This enables ones to eliminate the impact of noise (false positives and false negatives), which is particularly prevalent in online social networks and Web-scale information networks.Specifically we introduce a measure of signal strength between two nodes in the network by fusing their link strength with content similarity. Link strength is estimated based on whether the link is likely (with high probability) to reside within a community. Content similarity is estimated through cosine similarity or Jaccard coefficient. We discuss a simple mechanism for fusing content and link similarity. We then present a biased edge sampling procedure which retains edges that are locally relevant for each graph node. The resulting backbone graph can be clustered using standard community discovery algorithms such as Metis and Markov clustering.Through extensive experiments on multiple real-world datasets (Flickr, Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate the effectiveness and efficiency of our methods over state-of-the-art learning and mining approaches several of which also attempt to combine link and content analysis for the purposes of community discovery. Specifically we always find a qualitative benefit when combining content with link analysis. Additionally our biased graph sampling approach realizes a quantitative benefit in that it is typically several orders of magnitude faster than competing approaches.

Transactional data are ubiquitous. Several methods, including frequent itemset mining and co-clustering, have been proposed to analyze transactional databases. In this work, we propose a new research problem to succinctly summarize transactional databases. Solving this problem requires linking the high level structure of the database to a potentially huge number of frequent itemsets. We formulate this problem as a set covering problem using overlapped hyperrectangles (a concept generally regarded as tile according to some existing papers); we then prove that this problem and its several variations are NP-hard, and we further reveal its relationship with the Responsible editor: Bart Goethals. A preliminary version of this article appeared in 123 216 Y. Xiang et al.compact representation of a directed bipartite graph. We develop an approximation algorithm Hyper which can achieve a logarithmic approximation ratio in polynomial time. We propose a pruning strategy that can significantly speed up the processing of our algorithm, and we also propose an efficient algorithm Hyper+ to further summarize the set of hyperrectangles by allowing false positive conditions. Additionally, we show that hyperrectangles generated by our algorithms can be properly visualized. A detailed study using both real and synthetic datasets shows the effectiveness and efficiency of our approaches in summarizing transactional databases.

No abstract

Given a set of n query points in a general metric space, a metricspace skyline (MSS)

Stroke is a major cause of hemiparesis in United States. Constraint-Induced Movement therapy (CI therapy) is an effective treatment for upper extremity hemiparesis; however it is inaccessible to most patients. To make it more accessible, we developed a game-based rehabilitation system incorporating the major rehabilitation principles from CI therapy. We introduce a data analytics framework for our rehabilitation system in this paper that can provide objective measures of motor performance during gameplay. We design techniques of preprocessing collected data and propose a series of kinematic measurements, which are used to assess the motor performance and supplement in-clinic measures of therapeutic effect. We also present contextual filtering techniques to enable comparing movement production under different conditions, e.g., self-paced versus gamepaced movement. We apply our data analytics framework on data collected from several participants. Our analysis shows that participants' motor movement improves over the period of treatment, with different participants showing different patterns of improvement, e.g., speed versus range of motion. Results of kinematic measurements during gameplay are highly consistent with in-clinic performance based on the Wolf Motor Function Test. Moreover, our fine-grained trend analysis reveals potential to detect fatigue, which is related to the duration of gameplay.