Contrastive learning gains remarkable success in graph representation learning through imposing two properties to node representations, i.e., aligning representations of similar nodes in different augmented graphs and uniformly distributing node representations on the hypersphere. Existing graph contrastive learning methods capture these two key properties by extensive negative examples, resulting in unbearable computational costs. To overcome this issue, we propose a novel graph contrastive learning method, i.e., GraphALU, to capture these two properties without negative examples. For alignment, we leverage both self-alignment, i.e., pulling the representations of the same node in different augmented graphs together, and neighbor-alignment, pulling the representations of neighboring nodes in different augmented graphs together to capture the structure proximity among nodes. For uniformity, we innovatively design a star-shaped distance comparison, i.e., convert the distance comparison of any pair of nodes to the distance comparison of any nodes with a virtual center node, reducing the quadratic computational cost to a linear one with respect to the number of nodes. Extensive experiments show both the effectiveness and efficiency of GraphALU on various established benchmark datasets and downstream tasks.

Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.

Qualitative coding offers the potential to obtain deep insights into social media, but the technique can be inconsistent and hard to scale. Researchers using qualitative coding impose structure on unstructured data through “codes” that represent categories for analysis. Our visual analytics interface, Aeonium, supports human insight in collaborative coding through visual overviews of codes assigned by multiple researchers and distributions of important keywords and codes. The underlying machine learning model highlights ambiguity and inconsistency. Our goal was not to reduce qualitative coding to a machine-solvable problem, but rather to bolster human understanding gained from coding and reinterpreting the data collaboratively. We conducted an experimental study with 39 participants who coded tweets using our interface. In addition to increased understanding of the topic, participants reported that Aeonium's collaborative coding functionality helped them reflect on their own interpretations. Feedback from participants demonstrates that visual analytics can help facilitate rich qualitative analysis and suggests design implications for future exploration.

Online social media remarkably facilitates the production and delivery of information, intensifying the competition among vast information for users' attention and highlighting the importance of predicting the popularity of information. Existing approaches for popularity prediction fall into two paradigms: feature-based approaches and generative approaches. Feature-based approaches extract various features (e.g., user, content, structural, and temporal features), and predict the future popularity of information by training a regression/classification model. Their predictive performance heavily depends on the quality of hand-crafted features. In contrast, generative approaches devote to characterizing and modeling the process that a piece of information accrues attentions, offering us high ease to understand the underlying mechanisms governing the popularity dynamics of information cascades. But they have less desirable predictive power since they are not optimized for popularity prediction. In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process --- a widely-used generative process to model information cascade. DeepHawkes inherits the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades. We verify the effectiveness of DeepHawkes by applying it to predict retweet cascades of Sina Weibo and citation cascades of a longitudinal citation dataset. Experimental results demonstrate that DeepHawkes outperforms both feature-based and generative approaches.

This paper is about the aspects of ability, selfhood, and normalcy embodied in people's relationships with prostheses. Drawing on interviews with 14 individuals with upper-limb loss and diverse experiences with prostheses, we find people not only choose to use and not use prosthesis throughout their lives but also form close and complex relationships with them. The design of "assistive" technology often focuses on enhancing function; however, we found that prostheses played important roles in people's development of identity and sense of normalcy. Even when a prosthesis failed functionally, such as was the case with 3D-printed prostheses created by an on-line open-source maker community (e-NABLE), we found people still praised the design and initiative because of the positive impacts on popular culture, identity, and community building. This work surfaces crucial questions about the role of design interventions in identity production, the promise of maker communities for accelerating innovation, and a broader definition of "assistive" technology.

De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level.