Learning Edge Representations via Low-Rank Asymmetric Projections

We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the "graph likelihood", which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Analyzing and Learning Movement Through Human-Computer Co-Creative Improvisation and Data Visualization

Recent years have seen an incredible rise in the availability of household motion and video capture technologies, ranging from the humble webcam to the relatively sophisticated Kinect sensor. Naturally, this precipitated a rise in both the quantity and quality of motion capture data available on the internet. The wealth of data on the internet has caused a new interest in the field of motion data classification, the specific task of having a model classify and sort different clips of human motion. However, there is comparatively little work in the field of motion data clustering, which is an unsupervised field that may be more useful in the future as it allows for agents to recognize “categories” of motions without the need for user input or classified data. Systems that can cluster motion data focus more on “what type of motion data is this, and what is it similar to” rather than which motion is this. The LuminAI project, as described in this paper, is an example of a practical use for motion data clustering that allows the system to respond to user dance moves with a similar but different gesture. To analyze the efficacy and properties of this motion data clustering pipeline, we also propose a novel data visualization tool and the design considerations involved in its development.Undergraduat