Most self-supervised graph learning studies typically follow an offline training paradigm, assuming that all data are readily available.This assumption, however, is not always tenable in real-world scenarios as many graph data are generated continuously.Although several continual graph learning models have emerged and achieved empirical success, they almost all rely on external supervision, making it difficult to adapt to applications with a large amount of unlabeled data from the wild.To be honest, research on self-supervised continual graph learning is still surprisingly in its infancy.Therefore, we select several well-known self-supervised graph embedding models as representatives and explore whether they are resistant to catastrophic forgetting in a continual learning setting.Empirical studies find that self-supervised representation models may be potentially better continual learners than supervised counterparts.Driven by this advantage, we propose a self-supervised continual graph representation learning framework based on adaptive spaced replay on node proxies, named Trace.Inspired by the Complementary Learning System theory, Trace employs a dual-system architecture to simulate the functionality and cooperation of the hippocampus and neocortex in the brain.Among them, the fastlearning system efficiently encodes the current input graph to acquire new knowledge and adaptively extracts node proxies from it as important knowledge cached into the memory through progressive clustering.Drawing inspiration from the Ebbinghaus forgetting curve, the slow-learning system implements adaptive spaced replay based on the memory retention rate of each preceding task instead of the widely used consecutive replay scheme for promising flexibility and efficiency.Experiments under task-incremental and class-incremental learning settings on multiple datasets corroborate

Recently more and more cloud service providers (e.g., Microsoft, Google, and Amazon) have commercialized their well-trained deep learning models by providing limited access via web API interfaces. However, it is shown that these APIs are susceptible to model inversion attacks, where attackers can recover the training data with high fidelity, which may cause serious privacy leakage.Existing defenses against model inversion attacks, however, hinder the model performance and are ineffective for more advanced attacks, e.g., Mirror [4]. In this paper, we proposed NetGuard, a novel utility-aware defense methodology against model inversion attacks (MIAs). Unlike previous works that perturb prediction outputs of the victim model, we propose to mislead the MIA effort by inserting engineered fake samples during the training process. A generative adversarial network (GAN) is carefully built to construct fake training samples to mislead the attack model without degrading the performance of the victim model. Besides, we adopt continual learning to further improve the utility of the victim model. Extensive experiments on CelebA, VGG-Face, and VGG-Face2 datasets show that NetGuard is superior to existing defenses, including DP [37] and Ad-mi [32] on state-of-the-art model inversion attacks, i.e., DMI [8], Mirror [4], Privacy [12], and Alignment [34].

Accurate traffic anomaly prediction offers an opportunity to save the wounded at the right location in time. However, the complex process of traffic anomaly is affected by both various static factors and dynamic interactions. The recent evolving representation learning provides a new possibility to understand this complicated process, but with challenges of imbalanced data distribution and heterogeneity of features. To tackle these problems, this paper proposes a spatio-temporal evolution model namedSNIPERfor learning intricate feature interactions to predict traffic anomalies. Specifically, we design spatio-temporal encoders to transform spatio-temporal information into vector space indicating their natural relationship. Then, we propose a temporally dynamical evolving embedding method to pay more attention to rare traffic anomalies and develop an effective attention-based multiple graph convolutional network to formulate the spatially mutual influence from three different perspectives. The FC-LSTM is adopted to aggregate the heterogeneous features considering the spatio-temporal influences. Finally, a loss function is designed to overcome the ’over-smoothing’ and solve the imbalanced data problem. Extensive experiments show that SNIPER averagely outperforms state-of-the-arts by 3.9%, 0.9%, 1.9% and 1.6% on Chicago datasets, and 2.4%, 0.6%, 2.6% and 1.3% on New York City datasets in metrics of AUC-PR, AUC-ROC, F1 score, and accuracy, respectively.

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem, but it faces the following challenges: i) the inner correlations in user-item interaction data are difficult to capture from one side of the user or item; ii) capturing the knowledge associations on the whole KG would introduce noises and variously influence the recommendation results; iii) the semantic gap between both kinds of data is hard to alleviate.

In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies but fails to show the effectiveness throughout the entire interdependent network system. In this article, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.

People participate in multiple online social networks, e.g., Facebook, Twitter, and Linkedin, and these social networks with heterogeneous social content and user relationship are named as heterogeneous social networks. Group structure widely exists in heterogeneous social networks, which reveals the evolution of human cooperation. Detecting similar groups in heterogeneous networks has a great significance for many applications, such as recommendation system and spammer detection, using the wealth of group information. Although promising, this novel problem encounters a variety of technical challenges, including incomplete data, high time complexity, and ground truth. To address the research gap and technical challenges, we take advantage of a ratio-cut optimization function to model this novel problem by the linear mixed-effects method and graph spectral theory. Based on this model, we propose an efficient algorithm called D igger to detect the similar groups in the large graphs. D igger consists of three steps, including measuring user similarity, construct a matching graph, and detecting similar groups. We adopt several strategies to lower the computational cost and detail the basis of labeling the ground truth. We evaluate the effectiveness and efficiency of our algorithm on five different types of online social networks. The extensive experiments show that our method achieves 0.693, 0.783, and 0.735 in precision, recall, and F1-measure, which significantly surpass the state-of-arts by 24.4%, 15.3%, and 20.7%, respectively. The results demonstrate that our proposal can detect similar groups in heterogeneous networks effectively.