The recent surge in popularity of cloud-native applications using microservice architectures has led to a focus on accurate end-to-end latency prediction for proactive resource allocation. Existing models leverage Graph Transformers to Microservice Call Graphs or the Program Evaluation and Review Technique (PERT) graphs to capture complex temporal dependencies between microservices. However, these models incur a high computational cost during both training and inference phases. This paper introduces FastPERT, an efficient model for predicting end-to-end latency in microservice applications. FastPERT dissects an execution trace into several microservices tasks, using observations from prior execution traces of the application, akin to the PERT approach. Subsequently, a prediction model is constructed to estimate the completion time for each individual task. This information, coupled with the computational and structural inductive bias of the PERT graph, facilitates the efficient computation of the end-to-end latency of an execution trace. As a result, FastPERT can efficiently capture the complex temporal causality of different microservice tasks without relying on Graph Neural Networks, leading to more accurate and robust latency predictions across a variety of applications. An evaluation based on datasets generated from large-scale Alibaba microservice traces reveals that FastPERT significantly improves training and inference efficiency without compromising performance, demonstrating its potential as a superior solution for real-time end-to-end latency prediction in cloud-native microservice applications.

Graph Neural Networks (GNNs) are widely used as the engine for various graph-related tasks, with their effectiveness in analyzing graph-structured data. However, training robust GNNs often demands abundant labeled data, which is a critical bottleneck in real-world applications. This limitation severely impedes progress in Graph Anomaly Detection (GAD), where anomalies are inherently rare, costly to label, and may actively camouflage to evade detection. To address these problems, we propose Context Refactoring Contrast (CRoC), a simple yet effective framework that trains GNNs for GAD by jointly leveraging limited labeled and abundant unlabeled data. Unlike previous works, CRoC exploits the class imbalance inherent in GAD to refactor the context of each node, which builds augmented graphs by recomposing the attributes of nodes while preserving their interaction patterns. Furthermore, CRoC encodes heterogeneous relations separately and integrates them into the message-passing process, inducing the model to capture complex interaction semantics. These operations preserve node semantics while encouraging robustness against adverse camouflage, enabling GNNs to uncover intricate anomalous cases. In the training stage, CRoC is further integrated with the contrastive learning paradigm. This allows GNNs to effectively harness unlabeled data during joint training, producing richer, more discriminative node embeddings. CRoC is evaluated on seven real-world GAD datasets with different sizes. Extensive experiments demonstrate that CRoC achieves up to 14% AUC improvement over baseline GNNs and outperforms state-of-the-art GAD methods under limited-label settings.

Graph Neural Networks (GNNs) is a family of promising tools for graph semi-supervised learning. However, in training, most existing GNNs rely heavily on a large amount of labeled data, which is rare in real-world scenarios. Unlabeled data with useful information are usually under-exploited, which limits the representation power of GNNs. To handle these problems, we propose Virtual Overbridge Linking (Violin), a generic framework to enhance the learning capacity of common GNNs. By learning to add virtual overbridges between two nodes that are estimated to be semantic-consistent, labeled and unlabeled data can be correlated. Supervised information can be well utilized in training while simultaneously inducing the model to learn from unlabeled data. Discriminative relation patterns extracted from unlabeled nodes can also be shared with other nodes even if they are remote from each other. Motivated by recent advances in data augmentations, we additionally integrate Violin with the consistency regularized training. Such a scheme yields node representations with better robustness, which significantly enhances a GNN. Violin can be readily extended to a wide range of GNNs without introducing additional learnable parameters. Extensive experiments on six datasets demonstrate that our method is effective and robust under low-label rate scenarios, where Violin can boost some GNNs' performance by over 10% on node classifications.

Cloud-native applications using microservice architectures are rapidly replacing traditional monolithic applications. To meet end-to-end QoS guarantees and enhance user experience, each component microservice must be provisioned with sufficient resources to handle incoming API calls. Accurately predicting the latency of microservices-based applications is critical for optimizing resource allocation, which turns out to be extremely challenging due to the complex dependencies between microservices and the inherent stochasticity. To tackle this problem, various predictors have been designed based on the Microservice Call Graph. However, Microservice Call Graphs do not take into account the API-specific information, cannot capture important temporal dependencies, and cannot scale to large-scale applications.

Graph Neural Networks (GNNs) have recently become a popular framework for semi-supervised learning on graph-structured data. However, typical GNN models heavily rely on labeled data in the learning process, while ignoring or paying little attention to the data that are unlabeled but available. To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. By sharing the contextual information among nodes estimated to be in the same class, different nodes can be correlated even if they are unlabeled and remote from each other in the graph. Models can therefore learn different combinations of contextual patterns, which improves the robustness of node representations. Additionally, motivated by recent advances in self-supervised learning, we augment the context sharing strategy by integrating with contrastive learning, which naturally correlates intra-class and inter-class data. Such operations utilize all available data for training and effectively improve a model's learning capacity. CoCoS can be easily extended to a wide range of GNN-based models with little computational overheads. Extensive experiments show that CoCoS considerably enhances typical GNN models, especially when labeled data are sparse in a graph, and achieves state-of-the-art or competitive results in real-world public datasets. The code of CoCoS is available online.

Graph Neural Networks (GNNs) are the current state-of-the-art models in learning node representations for many predictive tasks on graphs. Typically, GNNs reuses the same set of model parameters across all nodes in the graph to improve the training efficiency and exploit the translationally-invariant properties in many datasets. However, the parameter sharing scheme prevents GNNs from distinguishing two nodes having the same local structure and that the translation invariance property may not exhibit in real-world graphs. In this paper, we present Graph Adaptive Mixtures (GraphAdaMix), a novel approach for learning node representations in a graph by introducing multiple independent GNN models and a trainable mixture distribution for each node. GraphAdaMix can adapt to tasks with different settings. Specifically, for semi-supervised tasks, we optimize GraphAdaMix using the Expectation-Maximization (EM) algorithm, while in unsupervised settings, GraphAdaMix is trained following the paradigm of contrastive learning. We evaluate GraphAdaMix on ten benchmark datasets with extensive experiments. GraphAdaMix is demonstrated to consistently boost state-of-the-art GNN variants in semi-supervised and unsupervised node classification tasks. The code of GraphAdaMix is available online.

Facial Expression Recognition (FER) is a challenging task in computer vision as features extracted from expressional images are usually entangled with other facial attributes, e.g., poses or appearance variations, which are adverse to FER. To achieve a better FER performance, we propose a model named Two-branch Disentangled Generative Adversarial Network (TDGAN) for discriminative expression representation learning. Different from previous methods, TDGAN learns to disentangle expressional information from other unrelated facial attributes. To this end, we build the framework with two independent branches, which are specific for facial and expressional information processing respectively. Correspondingly, two discriminators are introduced to conduct identity and expression classification. By adversarial learning, TDGAN is able to transfer an expression to a given face. It simultaneously learns a discriminative representation that is disentangled from other facial attributes for each expression image, which is more effective for FER task. In addition, a self-supervised mechanism is proposed to improve representation learning, which enhances the power of disentangling. Quantitative and qualitative results in both in-the-lab and in-the-wild datasets demonstrate that TDGAN is competitive to the state-of-the-art methods.

Facial expression recognition (FER) has long been a challenging task in computer vision. In this paper, we propose a novel method, named deep comprehensive multipatches aggregation convolutional neural networks (CNNs), to solve the FER problem. The proposed method is a deep-based framework, which mainly consists of two branches of the CNN. One branch extracts local features from image patches while the other extracts holistic features from the whole expressional image. In the model, local features depict expressional details and holistic features characterize the high-level semantic information of an expression. We aggregate both local and holistic features before making classification. These two types of hierarchical features represent expressions in different scales. Compared with most current methods with single type of feature, the model can represent expressions more comprehensively. Additionally, in the training stage, a novel pooling strategy named expressional transformation-invariant pooling is proposed for handling nuisance variations, such as rotations, noises, etc. Extensive experiments are conducted on the famous the Extended Cohn-Kanade (CK+) dataset and the Japanese Female Facial Expression (JAFFE) database expression datasets, where the recognition results obtained.

A novel framework, named intra‐class variation reduced features‐based manifold regularisation dictionary pair learning model, is presented for solving facial expression recognition (FER) tasks. Since a query face and its corresponding image with intra‐class variations (e.g. identity and illumination) are similar in appearance, the authors generate intra‐class variation reduced features (IVRF) from the difference between a query face image and its corresponding estimated image of each expression class. IVRF can reduce negative influence from the intra‐class variations and make their model robust to intra‐class variations. Furthermore, a manifold regularisation term is incorporated into the dictionary pair learning model, which leads to a smoothly varying sparse representation. Their model fully takes advantage of the geometrical structure of data, which benefits the FER task. The experimental results on two public databases verify the effectiveness and superiority of their method and indicate its promising capability in expression discrimination.

Facial expression recognition (FER) is a challenging task in the community of affect analysis and pattern recognition. In this paper, we propose a novel framework, namely Enhanced Dictionary Pair Learning Sparse Representation (EDPLSR), for facial expression recognition. The key idea behind our model is that it jointly learns a synthesis dictionary as well as an analysis dictionary, which require that all coding vectors should be group sparse. Furthermore, inspired by the observation that the geometrical information of the data is discriminative, a manifold regularization term is introduced to obtain smoothly vary sparse representations along the geodesics of data manifold. This is distinctive from most of the existing approaches which fail to consider the geometrical structure of data space. The experimental results demonstrate the effectiveness of our method.