Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions

Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates into a network all those elements that are driven by information which is external to the structure, like node properties or the flow of time. This paper proposes a novel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and feature-rich elements for representing, mining, and analyzing complex networks. Applied to social network analysis, ASHs can characterize complex social phenomena along topological, dynamic and attributive elements. Experiments on real-world face-to-face and online social media interactions highlight that ASHs can easily allow for the analyses, among others, of high-order groups' homophily, nodes' homophily with respect to the hyperedges in which nodes participate, and time-respecting paths between hyperedges.Comment: Submitted to "Applied Network Science

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page