3 accepted papers
We design a lightweight structural feature extraction technique for graph classification. It leverages node subsets and connection strength reflected by random-walk-based heuristics, presenting a scalable, unsupervised, and easily interpretable alternative. We provide theoretical insights into our t
We explore a novel problem in streaming submodular maximization, inspired by the dynamics of news-recommendation platforms. We consider a setting where users can visit a news web\-site at any time, and upon each visit, the web\-site must display up to $k$ news items. User interactions are inheren…
Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only…