Incomplete spatio-temporal data in real-world has spawned many research. However, existing methods often utilize iterative message-passing across temporal and spatial dimensions, resulting in substantial information loss and high computational cost. We provide a theoretical analysis revealing that such iterative models are not only susceptible to data sparsity but also to graph sparsity, causing unstable performances on different datasets. To overcome these limitations, we introduce a novel method named One-step Propagation and Confidence-based Refinement (OPCR). In the first stage, OPCR leverages inherent spatial and temporal relationships by employing sparse attention mechanism. These modules propagate limited observations directly to the global context through one-step imputation, which are theoretically effected only by data sparsity. Following this, we assign confidence levels to the initial imputations by correlating missing data with valid data. This confidence-based propagation refines the seperate spatial and temporal imputation results through spatio-temporal dependencies. We evaluate the proposed model across various downstream tasks involving highly sparse spatio-temporal data. Empirical results indicate that our model outperforms state-of-the-art imputation methods, demonstrating its superior effectiveness and robustness.

With the growing popularity of social media, malicious users (spammers) unfairly overpower legitimate users with unwanted or fake content to achieve their illegal purposes, which encourages research on spammer detection. The existing spammer detection methods can be characterized into feature-based detection and propagation-based detection. However, feature-based methods (e.g., GCN) cannot capture the user’s following relations, while propagation-based methods cannot utilize the rich text features. To this end, we consider combining these two methods and propose an Adaptive Reward Markov Random Field (ARMRF) layer. ARMRF layer models three intuitions on user label relations and assign them different learnable rewards. Besides, we learn the reward weights by stacking the ARMRF layer on top of GCN for end-to-end training, and we call the stacked model ARMGCN. To further improve the expressive power of ARMGCN, we propose the Markov-Driven Graph Convolutional Network (MDGCN), which integrates conditional random fields (CRF) and ARMGCN. CRF establishes the label joint probability distribution conditioned features for learning user dependencies, and the distribution can be optimized by a variational EM algorithm. We extensively evaluate the proposed method on two real-world Twitter datasets, and the experimental results demonstrate that MDGCN outperforms the state-of-the-art baselines. In addition, the ARMRF layer is model-independent, so it can be integrated with existing advanced detection methods to improve detection performance further.

Inspired by their powerful representation ability on graph-structured data, Graph Convolution Networks (GCNs) have been widely applied to recommender systems, and have shown superior performance. Despite their empirical success, there is a lack of theoretical explorations such as generalization properties. In this paper, we take a first step towards establishing a generalization guarantee for GCN-based recommendation models under inductive and transductive learning. We mainly investigate the roles of graph normalization and non-linear activation, providing some theoretical understanding, and construct extensive experiments to further verify these findings empirically. Furthermore, based on the proven generalization bound and the challenge of existing models in discrete data learning, we propose Item Mixture (IMix) to enhance recommendation. It models discrete spaces in a continuous manner by mixing the embeddings of positive-negative item pairs, and its effectiveness can be strictly guaranteed from empirical and theoretical aspects.

Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit citizens' quality of life and city planning. However, traffic anomaly detection faces two main challenges. First, it is challenging to model traffic dynamics due to the complex spatiotemporal characteristics of traffic data. Second, the criteria of traffic anomalies may vary with locations and times. In this article, we propose a spatiotemporal graph convolutional adversarial network (STGAN) to address these above challenges. More specifically, we devise a spatiotemporal generator to predict the normal traffic dynamics and a spatiotemporal discriminator to determine whether an input sequence is real or not. There are high correlations between neighboring data points in the spatial and temporal dimensions. Therefore, we propose a recent module and leverage graph convolutional gated recurrent unit (GCGRU) to help the generator and discriminator learn the spatiotemporal features of traffic dynamics and traffic anomalies, respectively. After adversarial training, the generator and discriminator can be used as detectors independently, where the generator models the normal traffic dynamics patterns and the discriminator provides detection criteria varying with spatiotemporal features. We then design a novel anomaly score combining the abilities of two detectors, which considers the misleading of unpredictable traffic dynamics to the discriminator. We evaluate our method on two real-world datasets from New York City and California. The experimental results show that the proposed method detects various traffic anomalies effectively and outperforms the state-of-the-art methods. Furthermore, the devised anomaly score achieves more robust detection performances than the general score.

The Four Great Classical Literatures(FGCL) including Treatise on Febrile Disease, Synopsis Golden Chamber, The Medical Classic of the Yellow Emperor, and Febrile disease defense are the basis of Chinese knowledge but were being used less and less because of the weak basis and education of the ancient Chinese medical language. For the purpose of letting more TCM doctors can comprehend FGCL better, a system was set up for the seamless connection between the arcane language in FGCL and the modern language. Each sentence in the FGCL mat contains formulas will be translated by clinic doctors using literatures reading books on three levels, (primary, middle, senior), by the standands of Terminology of traditional Chinese medicine clinical diagnosis and the systematized nomenclature of human medicine(SNOMED). Finally a Vertical professional search engine was bulit mat contains such parameters as symptoms, syndromes, famous discourses, typical case. The primary results was present on the web(see for http://www.jinhuatuo.com/biaozhun.php). After a period of clinical trials of the database, the system partly meets the need of searching the formulas in FGCL, but still need to be more associated and human-intelligent.