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As there are various data mining applications involving network analysis, network embedding is frequently employed to learn latent representations or embeddings that encode the network structure. However, existing network embedding models are only designed for a single network scenario. It is common that nodes can have multiple types of relationships in big data era, which results in multiple networks,...
Multi-view high-dimensional data become increasingly popular in the big data era. Feature selection is a useful technique for alleviating the curse of dimensionality in multi-view learning. In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain. Traditional feature selection methods are mostly designed for single-view data and cannot...
Community detection has been an important task for social and information networks. Existing approaches usually assume the completeness of linkage and content information. However, the links and node attributes can usually be partially observable in many real-world networks. For example, users can specify their privacy settings to prevent non-friends from viewing their posts or connections. Such incompleteness...
In this paper, we propose an Online unsupervised Multi-View Feature Selection method, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via nonnegative matrix factorization with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help...
Online reviews have become an increasingly important resource for decision making and product designing. But reviews systems are often targeted by opinion spamming. Although fake review detection has been studied by researchers for years using supervised learning, ground truth of large scale datasets is still unavailable and most of existing approaches of supervised learning are based on pseudo fake...
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