Strongly connected component (SCC) detection is fundamental for analyzing citation graphs, yet existing general-purpose algorithms inefficiently handle the dynamic nature and specific properties of these networks. This study addresses this gap by developing specialized incremental SCC detection methods. We first leverage distinct edge types inherent in citation graphs to devise partition and local topological ordering strategies, minimizing redundant graph traversals. Based on this, we introduce two efficient bounded incremental algorithms: one for continuous single updates via dynamic maintenance of partitions and order, and the other for batch updates that further reduces edge traversals by building upon the single-update technique. Experimental evaluations on real-world citation graphs verify significant efficiency improvements, with our single incremental method achieving speedups of at least 11.5 times, and the batch incremental method achieving speedups of at least 5.0 times compared with baseline methods.

$K$-nearest neighbor ( $K$NN) is one of the most fundamental methods for unsupervised outlier detection because of its various advantages, e.g., ease of use and relatively high accuracy. Currently, most data analytic tasks need to deal with high-dimensional data, and the $K$NN-based methods often fail due to “the curse of dimensionality”. AutoEncoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data, but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier detection. In this study, we propose to combine $K$NN with AutoEncoder for outlier detection. First, we propose the Nearest Neighbor AutoEncoder (NNAE) by persevering the original data proximity in a much lower dimension that is more suitable for performing $K$NN. Second, we propose the $K$-nearest reconstruction neighbors ( $K$NRNs) by incorporating the reconstruction errors of NNAE with the $K$-distances of $K$NN to detect outliers. Third, we develop a method to automatically choose better parameters for optimizing the structure of NNAE. Finally, using five real-world datasets, we experimentally show that our proposed approach NNAE+ $K$NRN is much better than existing methods, i.e., $K$NN, Isolation Forest, a traditional AutoEncoder using reconstruction errors (AutoEncoder-RE), and Robust AutoEncoder.

Scene-based recommendation has proven its usefulness in E-commerce, by recommending commodities based on a given scene. However, scenes are typically unknown in advance, which necessitates scene discovery for E-commerce. In this article, we study scene discovery for E-commerce systems. We first formalize a scene as a set of commodity categories that occur simultaneously and frequently in real-world situations, and model an E-commerce platform as a heterogeneous information network (HIN), whose nodes and links represent different types of objects and different types of relationships between objects, respectively. We then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the HIN. To solve the problem, we propose a non-negative matrix factorization based method SMEC (Scene Mining for E-Commerce), and theoretically prove its convergence. Using six real-world E-commerce datasets, we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods, and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices, trajectory data is continuously growing. While large-scale trajectories provide opportunities for various applications, the locations in trajectories pose a threat to individual privacy. Recently, there has been an interesting debate on the reidentifiability of individuals in the Science magazine. The main finding of Sánchez et al. is exactly opposite to that of De Montjoye et al., which raises the first question: "what is the true situation of the privacy preservation for trajectories in terms of reidentification?" Furthermore, it is known that anonymization typically causes a decline of data utility, and anonymization mechanisms need to consider the trade-off between privacy and utility. This raises the second question: "what is the true situation of the utility of anonymized trajectories?" To answer these two questions, we conduct a systematic experimental study, using three real-life trajectory datasets, five existing anonymization mechanisms (i.e., identifier anonymization, grid-based anonymization, dummy trajectories, k-anonymity and ε-differential privacy), and two practical applications (i.e., travel time estimation and window range queries). Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories, and essentially close the debate between De Montjoye et al. and Sánchez et al. To the best of our knowledge, this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.