Incremental maximal clique enumeration (IMCE), which maintains maximal cliques in dynamic graphs, is a fundamental problem in graph analysis. A maximal clique has a solid descriptive power of dense structures in graphs. Real-world graph data is often large and dynamic. Studies on IMCE face significant challenges in the efficiency of incremental batch computation and hybrid edge changes. Moreover, with growing graph sizes, new requirements occur on indexing global maximal cliques and obtaining maximal cliques under specific vertex scope constraints. This work presents a new data structure SOMEi to maintain intermediate maximal cliques during construction. SOMEi serves as a space-efficient index to retrieve scope-constrained maximal cliques on the fly. Based on SOMEi, we design a procedure-oriented IMCE algorithm to deal with hybrid edge changes within a unified algorithm framework. In particular, the algorithm is able to process a large batch of edge changes and significantly improve the average processing time of a single edge change through an efficient pruning strategy. Experimental results on real and synthetic graph data demonstrate that the proposed algorithm outperforms all the baselines and achieves good efficiency through pruning.

Irregularly, asynchronously and sparsely sampled multivariate time series (IASS-MTS) are characterized by sparse and uneven time intervals and nonsynchronous sampling rates, posing significant challenges for machine learning models to learn complex relationships within and beyond IASS-MTS to support various inference tasks. The existing methods typically either focus solely on single-task forecasting or simply concatenate them through a separate preprocessing imputation procedure for the subsequent classification application. However, these methods often ignore valuable annotated labels or fail to discover meaningful patterns from unlabeled data. Moreover, the approach of separate prefilling may introduce errors due to the noise in raw records, and thus degrade the downstream prediction performance. To overcome these challenges, we propose the time-aware dual attention and memory-augmented network (DAMA) with stochastic generative imputation (SGI). Our model constructs a joint task learning architecture that unifies imputation and classification tasks collaboratively. First, we design a new time-aware DAMA that accounts for irregular sampling rates, inherent data nonalignment, and sparse values in IASS-MTS data. The proposed network integrates both attention and memory to effectively analyze complex interactions within and across IASS-MTS for the classification task. Second, we develop the stochastic generative imputation (SGI) network that uses auxiliary information from sequence data for inferring the time series missing observations. By balancing joint tasks, our model facilitates interaction between them, leading to improved performance on both classification and imputation tasks. Third, we evaluate our model on real-world datasets and demonstrate its superior performance in terms of imputation accuracy and classification results, outperforming the baselines.

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.

The curse of dimensionality in high-dimensional data makes it difficult to capture the abnormality of data points in full data space. To deal with this problem, we propose an outlier detection model based on Variational Autoencoder and Genetic Algorithm for subspace outlier analysis of high-dimensional data (VAGA). The proposed VAGA model constructs a variational autoencoder (VAE) to preliminarily detect outliers. Then the genetic algorithm (GA) is used to search the abnormal subspace of the outliers obtained by the VAE layer to provide a basis for subspace outlier analysis. The subsequent clustering of the abnormal subspaces help filter out the false positives which are fed back to the VAE layer to adjust network weights. The comparative experiments performed on three public benchmark datasets show that the outlier detection results of the proposed VAGA model are highly interpretable and have better accuracy performance than the state-of-the-art outlier detection methods.