High-quality Representation Research Articles

NFGCL: A Negative-sampling-free Graph Contrastive Learning Framework for Recommendation

Recently, Graph Contrastive Learning (GCL) has seen rapid growth in recommender systems. GCL-based Collaborative Filtering (CF) methods typically integrate the primary recommendation task with auxiliary CL tasks, which benefit from two key properties: alignment and uniformity. However, these methods often rely on graph augmentation and negative sampling, which are time-consuming and risk learning incorrect signals. To remedy these issues, we propose a novel Negative-sampling-Free Graph Contrastive Learning (NFGCL) framework to achieve high-quality representations without negative sampling or graph augmentation. Specifically, we use in-batch positive user-item pairs to construct a preference matrix where each element represents cosine similarity between a user and an item. We introduce a novel contrastive objective to make the preference matrix close to an identity matrix, drawing positive pairs closer and distancing negative pairs to achieve better alignment while improving uniformity. Additionally, we propose a simple yet effective representation-level augmentation method that integrates the normalized first-layer Graph Convolutional Networks (GCN) output into the final embeddings, further enhancing alignment. We also employ the uniformity loss to regulate the uniformity of user/item representation distribution. Extensive experiments on three public datasets demonstrate that our method outperforms baseline methods and effectively balances alignment and uniformity.

Cross-modal embedding integrator for disease-gene/protein association prediction using a multi-head attention mechanism.

Knowledge graphs, powerful tools that explicitly transfer knowledge to machines, have significantly advanced new knowledge inferences. Discovering unknown relationships between diseases and genes/proteins in biomedical knowledge graphs can lead to the identification of disease development mechanisms and new treatment targets. Generating high-quality representations of biomedical entities is essential for successfully predicting disease-gene/protein associations. We developed a computational model that predicts disease-gene/protein associations using the Precision Medicine Knowledge Graph, a biomedical knowledge graph. Embeddings of biomedical entities were generated using two different methods-a large language model (LLM) and the knowledge graph embedding (KGE) algorithm. The LLM utilizes information obtained from massive amounts of text data, whereas the KGE algorithm relies on graph structures. We developed a disease-gene/protein association prediction model, "Cross-Modal Embedding Integrator (CMEI)," by integrating embeddings from different modalities using a multi-head attention mechanism. The area under the receiver operating characteristic curve of CMEI was 0.9662 (± 0.0002) in predicting disease-gene/protein associations. In conclusion, we developed a computational model that effectively predicts disease-gene/protein associations. CMEI may contribute to the identification of disease development mechanisms and new treatment targets.

The T2T Genome of the Domesticated Silkworm Bombyx mori.

Genome sequences contain the fundamental genetic information that largely determines the biology of a species. Over the past 20 years, advancements in high-throughput sequencing technologies and bioinformatics tools have matured, facilitating genome assembly and ushering in the telomere-to-telomere (T2T) era. Bombyx mori is renowned as a silk-producing insect and serves as an important model organism extensively studied across various fields of biology. In this study, we present the first assembled T2T genome by integrating HiFi, ultra-long ONT, NGS, and Hi-C data. This assembly comprises 450,267,439 base pairs from 28 chromosomes and includes annotations for a total of 18,253 protein-coding genes. A completeness evaluation revealed that 99.1% of conserved single-copy genes were included, as determined by a BUSCO analysis. Furthermore, the consensus quality (QV) assessed through Merqury was recorded at 59.88. The proportion of repeat sequence achieved 60.77%, marking it as the highest reported value for B. mori to date. In comparison to previously published genomes, our assembly offers a more complete and higher quality representation, particularly concerning highly homologous tandem regions such as telomeres, rDNA clusters, and Gr family regions. Furthermore, our extensive experience in genome assembly, including sample preparation experience and assembly strategies to reduce complexity, will provide valuable references for other species aiming to achieve their own T2T genome assemblies.

Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI

Background: Advances in medical image classification have recently benefited from general augmentation techniques. However, these methods often fall short in performance and interpretability. Objective: This paper applies the Unified Model Agnostic Computation (UMAC) framework specifically to the medical domain to demonstrate its utility in this critical area. Methods: UMAC is a model-agnostic methodology designed to develop machine learning approaches that integrate seamlessly with various paradigms, including self-supervised, semi-supervised, and supervised learning. By unifying and standardizing computational models and algorithms, UMAC ensures adaptability across different data types and computational environments while incorporating state-of-the-art methodologies. In this study, we integrate UMAC as a plug-and-play module within convolutional neural networks (CNNs) and Transformer architectures, enabling the generation of high-quality representations even with minimal data. Results: Our experiments across nine diverse 2D medical image datasets show that UMAC consistently outperforms traditional data augmentation methods, achieving a 1.89% improvement in classification accuracy. Conclusions: Additionally, by incorporating explainable AI (XAI) techniques, we enhance model transparency and reliability in decision-making. This study highlights UMAC’s potential as a powerful tool for improving both the performance and interpretability of medical image classification models.

Analysis of mapping atomic models to coarse-grained resolution.

Low-resolution coarse-grained (CG) models provide significant computational and conceptual advantages for simulating soft materials. However, the properties of CG models depend quite sensitively upon the mapping, M, that maps each atomic configuration, r, to a CG configuration, R. In particular, M determines how the configurational information of the atomic model is partitioned between the mapped ensemble of CG configurations and the lost ensemble of atomic configurations that map to each R. In this work, we investigate how the mapping partitions the atomic configuration space into CG and intra-site components. We demonstrate that the corresponding coordinate transformation introduces a nontrivial Jacobian factor. This Jacobian factor defines a labeling entropy that corresponds to the uncertainty in the atoms that are associated with each CG site. Consequently, the labeling entropy effectively transfers configurational information from the lost ensemble into the mapped ensemble. Moreover, our analysis highlights the possibility of resonant mappings that separate the atomic potential into CG and intra-site contributions. We numerically illustrate these considerations with a Gaussian network model for the equilibrium fluctuations of actin. We demonstrate that the spectral quality, Q, provides a simple metric for identifying high quality representations for actin. Conversely, we find that neither maximizing nor minimizing the information content of the mapped ensemble results in high quality representations. However, if one accounts for the labeling uncertainty, Q(M) correlates quite well with the adjusted configurational information loss, Îmap(M), that results from the mapping.

An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation.

Detecting side effects of drugs is a fundamental task in drug development. With the expansion of publicly available biomedical data, researchers have proposed many computational methods for predicting drug-side effect associations (DSAs), among which network-based methods attract wide attention in the biomedical field. However, the problem of data scarcity poses a great challenge for existing DSAs prediction models. Although several data augmentation methods have been proposed to address this issue, most of existing methods employ a random way to manipulate the original networks, which ignores the causality of existence of DSAs, leading to the poor performance on the task of DSAs prediction. In this paper, we propose a counterfactual inference-based data augmentation method for improving the performance of the task. First, we construct a heterogeneous information network (HIN) by integrating multiple biomedical data. Based on the community detection on the HIN, a counterfactual inference-based method is designed to derive augmented links, and an augmented HIN is obtained accordingly. Then, a meta-path-based graph neural network is applied to learn high-quality representations of drugs and side effects, on which the predicted DSAs are obtained. Finally, comprehensive experiments are conducted, and the results demonstrate the effectiveness of the proposed counterfactual inference-based data augmentation for the task of DSAs prediction.

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm

Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer’s disease.

Contrastive multi-interest graph attention network for knowledge-aware recommendation

Acquiring high-quality representations for both users and items is essential, facilitating a wide range of recommendation scenarios. Utilizing graph neural networks for knowledge-aware recommendation is a recent trend. However, there are two deficiencies in existing GNN-based knowledge-aware models: (1) They are coarse-grained in user representation, failing to capture the multi-interest distribution of users. (2) The supervised signals come only from historical interactions, failing to provide high-quality representations due to sparse data. In this paper, we propose a novel model, CMGAN with Contrastive Multi-interest Graph Attention Network, tailored for personalized knowledge-aware recommendations. Specifically, CMGAN employs a collaborative knowledge graph encoder, enhancing node representations through relational-aware embedding aggregation. Then a dynamic multi-interest generator crafts fine-grained multi-interest representations, adeptly extracting varied interests for each user based on their historical interactions. Furthermore, CMGAN innovates by integrating multi-level contrastive learning to refine representations at both node and multi-interest granularity. It consists of collaborative knowledge graph contrastive learning and multi-interest contrastive learning. The former pursues the acquisition of node representations that are more uniformly distributed, while the latter aims to obtain interest representations that are more distinct. A series of experiments on three benchmark datasets indicate that our model surpasses current state-of-the-art models. The code can be obtainable at https://github.com/liujianfang2021/CMGAN.

Self-supervised class-balanced active learning with uncertainty-mastery fusion

Deep active learning (DeepAL) offers a viable solution for enhancing the predictive performance of models with limited annotation costs. Popular DeepAL pipelines fail to (1) adequately and simultaneously draw on unlabeled and labeled samples to learn accurate data representations, and (2) progressively balance the distribution of selected samples across classes during the query. In this paper, we introduce a self-supervised DeepAL (SSAL) framework as a solution to the aforementioned challenges. First, SSAL simultaneously utilizes both labeled data and unlabeled data to learn high-quality representations. The method learns the image rotation degree for unlabeled data in a self-supervised manner, which can be jointly processed with the supervised learning. Second, the sample information is interpreted by fusing the discriminant uncertainty and the presumed mastery ability of each sample. This approach prevents the selection of many nonrepresentative boundary samples based solely on uncertainty and avoids the selection of aggregated samples based solely on clustering. Third, we propose a new sample selection strategy named class rebalanced group sampling. This strategy rebalances classes by the optimal size of query samples for each class, which is induced from the approximation between the population and the model distribution. Experiments were conducted on five public image datasets, namely CIFAR-10/100, FashionMNIST, SVHN, and TinyImageNet, and the results demonstrated the effectiveness of our approach. The code is available at https://github.com/FanSmale/SSAL.

Federated Momentum Contrastive Clustering

Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate a global model while training on distributed local data, respecting the data privacy of edge devices. However, most FL research effort focuses on supervised learning algorithms. A fully unsupervised federated clustering scheme has not been considered in the existing literature. We present federated momentum contrastive clustering (FedMCC), a generic federated clustering framework that can not only cluster data automatically but also extract discriminative representations training from distributed local data over multiple users. In FedMCC, we demonstrate a two-stage federated learning paradigm where the first stage aims to learn differentiable instance embeddings and the second stage accounts for clustering data automatically. The experimental results show that FedMCC not only achieves superior clustering performance but also outperforms several existing federated self-supervised methods for linear evaluation and semi-supervised learning tasks. Additionally, FedMCC can easily be adapted to ordinary centralized clustering through what we call momentum contrastive clustering (MCC). We show that MCC achieves state-of-the-art clustering accuracy results in certain datasets such as STL-10 and ImageNet-10. We also present a method to reduce the memory footprint of our clustering schemes.

High-quality use of representations in the mathematics classroom – a matter of the cultural perspective?

The teacher’s use of representations is a crucial aspect of instructional quality in mathematics education, given their pivotal role in facilitating mathematics learning. However, in our international research community, perspectives on what constitutes high-quality use of representations may vary. This cross-cultural study aims to explore whether the perspectives from Western literature, emphasizing the importance of explicit connections between symbolic and graphic representations, can be extended legitimately to the East Asian context. Using a situated approach, the study elicited norms of high-quality representation use from researchers in Germany and Taiwan. A total of 31 mathematics education professors from both countries evaluated the use of representations in three secondary mathematics classroom situations presented as text vignettes. The vignettes, designed by the German research team, each depicted a situation where from their perspective, a norm of high-quality representation use, specifically the explicit connection between symbolic and graphic representations, was violated. Qualitative analysis of the researchers' responses revealed that in each situation, at least half of the German researchers expected explicit connections between representations. Conversely, the majority of Taiwanese researchers only expected such connections in one situation, particularly when the graphic representation served as an independent learning objective rather than solely aiding conceptual understanding. These findings indicate easily unnoticed culture-specific differences regarding how a common aspect of instructional quality is expected to unfold in teaching.

Doing right by families: Presenting dynamic performance metrics for legal excellence in child welfare law

AbstractTo meet the challenge of advancing child welfare law, a field affecting millions of families a year, practicing attorneys have developed a wealth of guidance for best practices. The Judicial, Court, and Attorney Measures of Performance (JCAMP) provide a way to determine whether legal programs are following these recommended practices and to assess the strength of attorney–client relationships. After providing context around challenges to high quality legal representation, best practice guidance, and evaluation research findings, this article reviews the JCAMP measures of attorney practice. JCAMP may be used by legal programs to both assess their own practice and to elicit feedback from affected families to ensure consistent high quality representation. Boldly assessing attorney interactions, and the experiences of clients in the court process, is critical to guiding and empowering lawyers to achieve excellence.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

When remembering less is more: Unfiltered items are associated with reduced memory fidelity in visual short-term memory.

Visual short-term memory (VSTM), the ability to store information no longer visible, is essential for human behavior. VSTM limits vary across the population and are correlated with overall cognitive ability. It has been proposed that low-memory individuals are unable to select only relevant items for storage and that these limitations are greatest when memory demands are high. However, it is unknown whether these effects simply reflect task difficulty and whether they impact the quality of memory representations. Here we varied the number of items presented, or set size, to investigate the effect of memory demands on the performance of visual short-term memory across low- and high-memory groups. Group differences emerged as set size exceeded memory limits, even when task difficulty was controlled. In a change-detection task, the low-memory group performed more poorly when set size exceeded their memory limits. We then predicted that low-memory individuals encoding items beyond measured memory limits would result in the degraded fidelity of memory representations. A continuous report task confirmed that low, but not high, memory individuals demonstrated decreased memory fidelity as set size exceeded measured memory limits. The current study demonstrates that items held in VSTM are stored distinctly across groups and task demands. These results link the ability to maintain high quality representations with overall cognitive ability.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view

Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner’s personal information and the courses’ teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics.

Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation

Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. Therefore, the first one involves proposing an effective neural network as the denoising kernel that is capable to capture complex multi-body interatomic relationships and learn high-quality features. Due to the discrete nature of graphs, mainstream diffusion-based methods for molecules heavily rely on predefined rules and generate edges in an indirect manner. The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds. In our research, we view the iterative way of updating molecule conformations in diffusion process is consistent with molecular dynamics and introduce a novel molecule generation method named Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge, we introduce a Dual-track Transformer Network (DTN) to fully excevate global spatial relationships and learn high quality representations which contribute to accurate predictions of features and geometries. As for the second challenge, we design Geometric-facilitated Loss (GFLoss) which intervenes the formation of bonds during the training period, instead of directly embedding edges into the latent space. Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff.

Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://github.com/lijy373/CCLIS.

Rethinking Graph Masked Autoencoders through Alignment and Uniformity

Self-supervised learning on graphs can be bifurcated into contrastive and generative methods. Contrastive methods, also known as graph contrastive learning (GCL), have dominated graph self-supervised learning in the past few years, but the recent advent of graph masked autoencoder (GraphMAE) rekindles the momentum behind generative methods. Despite the empirical success of GraphMAE, there is still a dearth of theoretical understanding regarding its efficacy. Moreover, while both generative and contrastive methods have been shown to be effective, their connections and differences have yet to be thoroughly investigated. Therefore, we theoretically build a bridge between GraphMAE and GCL, and prove that the node-level reconstruction objective in GraphMAE implicitly performs context-level GCL. Based on our theoretical analysis, we further identify the limitations of the GraphMAE from the perspectives of alignment and uniformity, which have been considered as two key properties of high-quality representations in GCL. We point out that GraphMAE's alignment performance is restricted by the masking strategy, and the uniformity is not strictly guaranteed. To remedy the aforementioned limitations, we propose an Alignment-Uniformity enhanced Graph Masked AutoEncoder, named AUG-MAE. Specifically, we propose an easy-to-hard adversarial masking strategy to provide hard-to-align samples, which improves the alignment performance. Meanwhile, we introduce an explicit uniformity regularizer to ensure the uniformity of the learned representations. Experimental results on benchmark datasets demonstrate the superiority of our model over existing state-of-the-art methods. The code is available at: https://github.com/AzureLeon1/AUG-MAE.