Drift Problem Research Articles

Drift Correction Method of Wastewater Treatment Model Based on Transfer Learning Across Time Scales

Data is the core foundation of intelligent operation and maintenance, but currently, there is generally insufficient data for wastewater treatment plants, and the status of wastewater treatment systems dynamically evolves with the changes in the internal and external environment. The intelligent operation and maintenance of wastewater plants face difficulties in modeling and model drift caused by system evolution. In response to this issue, the summer and winter seasons with significant differences in wastewater temperature, wastewater quality, and microbial status were selected as typical comparison scenarios. The mechanism model was combined with neural networks to establish a wastewater treatment model drift correction method based on cross-time scale transfer learning. Firstly, in response to the problem of insufficient data, an activated sludge model （ASM） was established and calibrated. Summer operating data was used as input to simulate and calculate operating parameters and effluent data, generating a simulated operating dataset to achieve data augmentation and quality improvement. This was used to train a multi-layer perceptron neural network （MLP） model. The results showed that the average simulation accuracy of the MLP model for summer effluent COD, ammonia nitrogen, total phosphorus, etc., was all over 95%. This indicates the feasibility of training MLP models based on ASM-generated data. Then, the MLP model was used to guide the operation of the pilot A2O project. Experimental data analysis showed that the model drift phenomenon was significant in the field of wastewater treatment. During the operation of the pilot plant guided by the summer model, the accuracy of the predicted values gradually decreased, and the average prediction accuracy of the model for effluent COD gradually decreased from 98.14% to 75.18%. The phenomenon of model drift required effective correction to maximize the effectiveness of the model. In response to the problem of model drift caused by a significant decrease in simulation accuracy in winter operating conditions, a transfer learning approach was introduced. The winter measured data was used as the target domain dataset, and the MLP model was used as the pre-trained model for transfer learning. The experimental results showed that transfer learning methods can significantly improve model performance. After transfer learning, the average simulation accuracy of the MLP model for effluent COD, ammonia nitrogen, total nitrogen, and total phosphorus was relatively improved by 28.58%, 184.44%, 207.56%, and 100.51%, with absolute improvement values of 21.49%, 60.79%, 58.14%, and 46.74%, respectively. This indicates that the cross-time scale transfer learning method proposed in this study can significantly improve model performance, effectively solve model drift problems, and achieve a model-following response to the dynamic evolution of wastewater treatment systems. This study indicates that transfer learning based on pre-trained models only requires a small amount of engineering data and computational complexity to achieve model updating and correction. Compared to model retraining, this method reduces computational complexity and reduces the dependence on engineering data during data-driven model updates.

Multi global context‐aware transformer for ship name recognition in IoT

AbstractScene text recognition has gained increasing attention in recent years, as it can connect products without an open interface in IoT. The non‐local network is particularly popular in text recognition, as it can aggregate the temporal message of the input. However, existing text recognition methods based on RNN encoder‐decoder structures encounter the problem of attention drift, especially in complex ship name recognition scenarios, because the features extracted by these methods are extremely similar. To address this problem, this paper proposes a novel text recognition approach named Multi Global Context‐aware Transformer (MG‐Cat). The proposed approach has two main properties: (1) a Global Context block that captures the global relationships among pixels inside the encoder, and (2) multiple global context‐aware attention modules stacked in the encoder process. This way, the MG‐Cat approach can learn a more robust intermediate feature representation in the text recognition pipeline. Moreover, the paper collected a new ship name dataset to evaluate the proposed approach. Extensive experiments were conducted on the collected dataset to verify the effectiveness of the proposed approach. The experimental results show the generalization ability of our squeeze‐and‐excitation global context attention module.

Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

This paper is dedicated to solving the problem of concept drift in industrial plants using artificial intelligence methods. For this purpose, methodological approaches and procedures are considered and analyzed. Based on the findings, reference architectures were developed at different abstraction levels that can be used in an industrial environment and enable continuous machine learning. Continuous machine learning offers the possibility of adapting to dynamic changes in the production environment, which are reflected in constantly changing data sets. Through a combination of machine learning techniques, a novel and practical framework for continuous learning, also known as lifelong learning, is presented. The integration of problem-focused machine learning methods is advancing in production, e.g., predictive maintenance, process optimization or fault detection. Thereby, fully or semi-automated adaptations to changing environments requiring continuous improvements are less often explored, although practical use cases often require adaptive capabilities as the physical data distribution may change over time. In this paper, the application was continuously improved based on case studies and empirical results, and finally validated with a quality assurance application. Various methods and approaches for detecting concept and data deviations, retraining, packaging and model updating had to be investigated, which led to the question of what a real industry-oriented implementation could look like. The result is a reference architecture that can run on cloud and edge computing resources. This reference architecture is validated in real-world application in the parquet production sector, proving its feasibility and efficiency.

Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman Filter.

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments. The fusion filter gains in FIS-IESKF are adaptively adjusted for noise predictions, optimizing the rule parameters of the fuzzy inference process. Experimental results demonstrate that the proposed method effectively enhances the localization accuracy and system robustness of differential-drive indoor mobile robots in dynamically changing movements and environments.

Law school culture and curriculum: Correcting social justice drift

This article examines the problem of social justice drift among law students. The author discusses institutional measures to reverse this trend and identifies shortcomings of existing policies. Noting that the focus has been trained on expanding social justice-oriented curricular and extracurricular offerings, the author contends that mainstreaming social justice in the core curriculum of professionally accredited law degrees is more likely to prevent the attenuation of social justice commitment among students and better equip the next generation of legal practitioners to pursue careers in social justice. Options for curricular reform to this end are duly considered.

Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions

In recent years, unsupervised domain adaptation (UDA) has gained widespread application in addressing intelligent fault diagnosis under variable operating conditions. However, how to effectively model data structure information and integrate it into UDA has hindered the application of intelligent fault diagnosis in the industry. To solve this issue, a multi-scale and multi-structure information-embedded unsupervised graph transfer network for fault diagnosis is proposed. Specifically, a novel node feature extractor is first designed for feature embedding. To better fuse multi-scale information and obtain more effective features, a multi-scale convolutional layer and a hybrid attention module are utilized. Secondly, an adaptive similarity graph-constructing method based on the inner-product kernel is adopted to convert the node features into graph data. Next, the graph neural network (GNN) is introduced to obtain graph-structured information. Finally, a joint domain adaptation module is designed to cope with the covariance drift problem in cross-domain fault diagnosis. The proposed method exhibited state-of-the-art performance in the experiments of three case studies.

Novelty-aware concept drift detection for neural networks

Neural network models are widely adopted in real-world applications for processing streaming data. However, these applications often face challenges in terms of accuracy degradation, caused by changes in the data distribution of the stream data compared to the training data. Two underlying reasons contribute to these changes. The first, known as the concept drift problem, occurs when there is a change in the correlation between the input data and the prediction output, making the models trained on the training data no longer suitable for the new data. The second reason, known as the novelty problem, arises when real-world data contains unexpected data categories that were not present in the training data, resulting in incorrect predictions. The research community has divided into different groups and each developed various methods to detect either concept drift or novelty distribution changes. However, these methods only address one aspect of the problem and are unable to distinguish between them. This leads to an inappropriate allocation of model maintenance resources, including the high cost of model retraining and the acquisition of true label data. In this study, we aim to address this gap by proposing a novel concept drift detection method that is capable of distinguishing between known labeled concept drift and novelty. Our method is also more efficient than existing drift detection methods, making it suitable for applications on neural networks.

Transferring knowledge by budget online learning for multiobjective multitasking optimization

Multiobjective multitasking optimization (MO-MTO) has attracted increasing attention in the evolutionary computation field. Evolutionary multitasking (EMT) algorithms can improve the overall performance of multiple multiobjective optimization tasks through transferring knowledge among tasks. Negative transfer resulting from the indeterminacy of the transferred knowledge may bring about the degradation of the algorithm performance. Identifying the valuable knowledge to transfer by learning the historical samples is a feasible way to reduce negative transfer. Taking this into account, this paper proposes a budget online learning based EMT algorithm for MO-MTO problems. Specifically, by regarding the historical transferred solutions as samples, a classifier would be trained to identified the valuable knowledge. The solutions which are considered containing valuable knowledge will have more opportunity to be transfer. For the samples arrive in the form of streaming data, the classifier would be updated in a budget online learning way during the evolution process to address the concept drift problem. Furthermore, the exceptional case that the classifier fails to identify the valuable knowledge is considered. Experimental results on two MO-MTO test suits show that the proposed algorithm achieves highly competitive performance compared with several traditional and state-of-the-art EMT methods.

Soft Prompt-tuning with Self-Resource Verbalizer for short text streams

Short text streams such as real-time news and search snippets have attained vast amounts of attention and research in recent decades, the characteristics of high generation velocity, feature sparsity, and high ambiguity accentuate both the importance and challenges to language models. However, most of the existing short text stream classification methods can neither automatically select relevant knowledge components for arbitrary samples, nor expand knowledge internally instead of rely on external open knowledge base to address the inherent limitations of short text stream. In this paper, we propose a Soft Prompt-tuning with Self-Resource Verbalizer (SPSV for short) for short text stream classification, the soft prompt with self-resource knowledgeable expansion is conducted for updating label words space to address evolved semantic topics in the data streams. Specifically, the automatic constructed prompt is first generated to instruct the model prediction, which is optimized to address the problem of high velocity and topic drift in short text streams. Then, in each chunk, the projection between category names and label words space, i.e. verbalizer, is updated, which is constructed by internal knowledge expansion from the short text itself. Through comprehensive experiments on four well-known benchmark datasets, we validate the superb performance of our method compared to other short text stream classification and fine-tuning PLMs methods, which achieves up to more than 90% classification accuracy with the counts of data chunk increased.

A Robust Semi-Supervised Broad Learning System Guided by Ensemble-Based Self-Training.

Broad learning system (BLS) with semi-supervised learning relieves label dependence and expands application. Despite some efforts and progress, the semi-supervised BLS still needs improvement, especially in handling imbalanced data or concept drift scenarios for self-training-based methods. To this extent, this article proposes a robust semi-supervised BLS guided by ensemble-based self-training (ESTSS-BLS). Distinctive to self-training that assigns the pseudo-label via a single classifier and confidence, the advocated ensemble-based self-training determines the pseudo-label according to the turnout of multiple BLSs. In addition, label purity is proposed to ensure the correctness and credibility of the auxiliary training data, which is a comprehensive evaluation of the voting. During iterative learning, a small portion of labeled data first trains multiple BLSs in parallel. Then, the system recursively updates its data, structure, and meta-parameters using label purity and a data-driven dynamic nodes mechanism that dynamically guides the network's structural adjustments to solve the concept drift problem caused by a large amount of auxiliary training data. The experimental results demonstrate that ESTSS-BLS exhibits exceptional performance compared to existing methods, with the lowest-time consumption and the highest accuracy, precision, recall, F1 score, and AUC. Exhilaratingly, it achieves an accuracy of 87.84% with only 0.1% labeled data on MNIST, and with just 2% labeled data, it matches the performance of supervised learning using all training data on NORB. In addition, ESTSS-BLS also performs stably on medical or biological data, verifying its high adaptability.

High-efficiency MPPT strategy for PV Systems: Ripple-free precision with comprehensive simulation and experimental validation

This paper presents a newly developed maximum power point (MPP) tracking algorithm (MPPT) to boost the tracking performance of solar photovoltaic (PV) systems. By functioning PV arrays at their MPP and eliminating the ripple problem in the converter's output, the newly developed strategy can markedly improve the precision of tracking and overcome the issues faced by many existing algorithms. The proposed strategy uses an MPP voltage boundary to control the PV voltage and directly generate the required duty cycle using a mathematic expression, resulting in a high convergence speed, drift problem avoidance, and zero MPP fluctuations. To prove and validate the robustness of tracking of the suggested MPPT strategy, both simulation and experiment validations based on the MATLAB/Simulink and dSPACE DS1104 board platforms, respectively, are carried out under swift variations in solar irradiance. The effectiveness of the proposed approach is evaluated by comparing it to the InC algorithm in terms of tracking accuracy. The results from both simulations and experiments demonstrate that the suggested strategy surpasses the InC approach in multiple aspects. It exhibits a shorter response time, eliminates the ripple problem, and achieves a superior tracking efficiency of 99.60 %.

Semi-supervised comparative learning compensation method for chemical gas sensor drift.

The gradual and unpredictable variation in chemo-sensory signal responses when exposed to the same analyte under identical conditions, commonly referred to as sensor drift, has long been recognized as one of the most serious challenges faced by chemical sensors. The traditional drift compensation method is both labor-intensive and expensive, as it requires frequent collection and labeling of gas samples for recalibration. Introducing a small number of meaningful drift calibration samples can be an attractive strategy to reduce the computational load and improve the performance of the updated classifier. However, under the influence of drift, new challenges arise due to the difference in the distribution of source and target domain data. This paper proposes a novel algorithm framework called semi-supervised contrastive learning drift compensation (SSCLDC). The framework automatically extracts high-level abstract features based on a multilayer perceptron to better represent the structure of the source data. In addition, to address the issue of data distribution differences caused by drift between the source and target domains. We add a small number of reference sample pairs into the training for semi-supervised learning. Combining a contrastive loss function that can represent the matching degree of paired samples effectively overcomes the problem of sensor drift. The Kennard-Stone sequential algorithm is used to select the representative reference sample from the set of candidate reference samples. Experiments conducted on a widely used long-term chemical gas sensor drift dataset demonstrate that the proposed method outperforms several classic drift compensation techniques, highlighting its effectiveness and practical applicability.

Occluded vehicle tracking by integrating full-scale features and trajectory correction

In order to improve the tracking drift and identity switching (IDs) problems caused by occlusion in vehicle tracking, a method for tracking occluded vehicles that integrates full-scale features and trajectory correction is proposed based on the Deep SORT algorithm. Firstly, a full-scale feature extraction network is introduced to extract the features of different scales of the target and realize adaptive dynamic fusion to enhance the target appearance characteristics. Then, a trajectory correction algorithm is proposed to repair the tracking trajectory during occlusion, and the Kalman filter parameters are updated to reduce the accumulated linear error during occlusion and optimize the target motion characteristics. Finally, the occluded vehicle is tracked by combining appearance features and motion features. The feasibility of the proposed method is verified by ablation experiments and visualization analysis. The experimental results on the KITTI dataset show that compared with the existing typical methods, the proposed method achieves the highest comprehensive score of 78.13 and the lowest number of IDs of 192, which effectively improves the IDs problem in occluded vehicle tracking and improves the robustness of vehicle tracking.

Cuffless Wearable Sphygmomanometers Using Dual Digital Optical Frequency Combs in Chalcogenide Chips

AbstractSphygmomanometers typically use a rubber cuff around the upper arm to measure blood pressure, but this method can be uncomfortable. Thus, cuffless sphygmomanometers for continuous blood pressure monitoring in emergencies and healthcare are desirable. While electrical and optical blood pressure sensors offer high sensitivity, they often face issues like limited pressure range, temperature drift, slow response, and wearability problems. To tackle these limitations, the study designs a unique cuffless wearable blood pressure sensor (12 mm × 2 mm × 0.5 mm) featuring two original components: a GeSbS/AsS chalcogenide dual‐microring chip for high sensitivity, and a dual digital optical frequency comb (DDOFC) system for fast response and cost‐effective detection. The sensor is comparable to reported electrical and optical blood pressure sensors in its detection limit (23 Pa) but has a ten‐times faster response rate (2 kHz), a 1000‐times longer stable operation (>4.3 × 107 cycles), and a well‐compensated temperature drift (<0.012 pm/°C) that eliminates the need for temperature control. This innovation shows promise for wearable electronics and mobile healthcare, enabling the capture of critical cardiovascular details for applications like in‐surgery monitoring, orthostatic hypotension studies, and early detection of myocardial ischemia and strokes.

An optimized isolation forest based intrusion detection system for heterogeneous and streaming data in the industrial Internet of Things (IIoT) networks

While conventional Intrusion Detection Systems (IDS) are essential for defending against intruders in the Industrial Internet of Things (IIoT), handling data from heterogeneous and streaming data sources should receive more attention. This work introduces a novel Optimized IForest-based Intrusion Detection System (OIFIDS) which is designed to handle both heterogeneous and streaming data efficiently. The suggested approach employs a collection of optimized binary trees, each of which is trained on a distinctive subset of data, and in which the location of empty leaves determines the anomaly score assigned to a certain data point. Optimizing isolation Forest (iForest) utilizing a modified version of the Harris Hawks Optimization algorithm, which exploits both Exploration factor and Random walk strategies (ERHHO) decreases the dataset's dimension, decreases its learning time, and enhances the detection precision, accuracy, F1-score, FPR, and recall. To demonstrate how effective the proposed approach is, it is evaluated using three datasets: CICIDS-2018, NSL-KDD, and UNSW-NB15. The experimental results prove the ability of the suggested approach in handling both heterogeneous and streaming data efficiently and delivering results that were comparable to the cutting-edge baseline techniques. Moreover, it performs effectively when there are no anomalies in the training sample and when dealing with challenging scenarios with several irrelevant features and high dimensions. Based on the comparison with various state-of-the-art IDSs, the suggested approach is able to detect intrusion with greater accuracies of 95.6%, 94.8%, and 99% than the other approaches on the NSL-KDD, UNSW-NB15, and CICIDS-2018 datasets, respectively. Experiments on heterogenous data reveal that Area Under the ROC Curve (AUC) of OIFIDS beats the baseline approach for UNSW-NB15 dataset and is higher than the second-best method by 8% and 2.4% for NSL-KDD and CICIDS-2018 respectively. Evaluating the proposed system on streaming data illustrates that it can address the concept drift problem well with high AUC value of 0.948, 0.97, and 0.922 on the NSL-KDD, CICIDS-2018, and UNSW-NB15 datasets, respectively.

Design of MEMS Pressure Sensor Anti-Interference System Based on Filtering and PID Compensation.

Due to the inherent temperature drift and lack of static stability in traditional pressure sensors, which make it difficult for them to meet the increasing demands of various industries, this paper designs a new system. The proposed system integrates temperature measurement and regulation circuits, signal processing, and communication circuits to accurately acquire and transmit pressure sensor data. The system designs a filtering algorithm to filter the original data and develops a data-fitting operation to achieve error compensation of the static characteristics. In order to eliminate the temperature drift problem of the sensor system, the system also adopts an improved PID thermostatic control algorithm to compensate for the temperature drift. Finally, it can also transmit the processed pressure data remotely. The experimental results show that the nonlinear error at 50 °C is reduced from the initial 1.82% to 0.24%; the hysteresis error is significantly reduced from 1.23% to 0.046%; and the repeatability error control is reduced from 3.79% to 0.89%. By compensating for thermal drift, the system's thermal sensitivity drift coefficient is reduced by 74.67%, the thermal zero drift coefficient is reduced by 66.24%, and the wireless communication range is up to 1km. The above significant optimization results fully validate the high accuracy and stability of the system, which is perfectly suited for demanding pressure measurement applications.

Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.

Entropy-guided robust feature domain adaptation for electroencephalogram-based cross-dataset drowsiness recognition

To accurately identify driver drowsiness using Electroencephalogram (EEG), a significant amount of time is typically required to gather data from either the same subject or multiple subjects under the same experimental condition. The prolonged period required for data collection impedes the practical application of EEG. In this paper, we consider the challenging task of cross-dataset driver drowsiness recognition, where the EEG data collected by different devices are expected to be conveniently recognized by a classifier trained on an existing dataset. In order to solve the problem of distribution drift, we propose an Entropy-Guided Robust Feature (EGRF) adaptation framework. This framework utilizes the Entropy Minimization (EM) technique for domain alignment and an ensemble-like structure, consisting of multiple branch classifiers and a dropout layer, to enhance the robustness. The proposed method has been tested across two public datasets and achieves 2-class recognition accuracies of 77.4% and 85.9%. The innovative feature-robust block, combined with the EM loss, leads to substantial performance improvement, proving the effectiveness of our framework for cross-dataset driver drowsiness recognition. Our research demonstrates a promising direction for development of a calibration-free driver drowsiness monitoring system in the future.

Generalized Federated Learning via Gradient Norm-Aware Minimization and Control Variables

Federated Learning (FL) is a promising distributed machine learning framework that emphasizes privacy protection. However, inconsistencies between local optimization objectives and the global objective, commonly referred to as client drift, primarily arise due to non-independently and identically distributed (Non-IID) data, multiple local training steps, and partial client participation in training. The majority of current research tackling this challenge is mainly based on the empirical risk minimization (ERM) principle, while giving little consideration to the connection between the global loss landscape and generalization capability. This study proposes FedGAM, an innovative FL algorithm that incorporates Gradient Norm-Aware Minimization (GAM) to efficiently search for a local flat landscape. FedGAM specifically modifies the client model training objective to simultaneously minimize the loss value and first-order flatness, thereby seeking flat minima. To directly smooth the global flatness, we propose the more significant FedGAM-CV, which employs control variables to correct local updates, guiding each client to train models in a globally flat direction. Experiments on three datasets (CIFAR-10, MNIST, and FashionMNIST) demonstrate that our proposed algorithms outperform existing FL baselines, effectively finding flat minima and addressing the client drift problem.

Self-X heterogeneous attributed graph embedding-based product configuration framework for cognitive mass personalization

Cognitive mass personalization (CMP) is a promising manufacturing paradigm; equipped with cognitive capabilities like reasoning, CMP satisfies changeable needs via configuring personalized products at scale. In CMP, knowledge graphs (KGs) are exploited by smart product-service systems (SPSS) to support cognitive configuration/reconfiguration processes. However, the extant KG-enabled SPSSs are built upon fixed configurations and hybrid frameworks due to lacking a graph embedding (GE) model to render cognitive configuration decisions. In fact, GE is scarcely used in SPSS configuration, because it is not only compromised by the heterogeneity of KGs entailed by content-related specifications and complex structures but also influenced by the feature randomness and feature drift problems, which are triggered by accumulative errors and inconsistent objectives due to noisy assignments and different configuration tasks, separately. To address these limitations, a Self-X Heterogeneous Attributed Graph Embedding (SXHAGE) model is proposed in a Self-X architecture, which includes 1) self-attention graph attention networks, 2) a self-adaptive autoencoder, and 3) self-optimizing training objectives, to present heterogeneous data through jointly optimizing heterogeneous attributed entities and relations. A systematic SXHAGE-based configuration framework, in which product family design and configuration recommending are enabled by graph clustering and link prediction, is developed as a continuous updating loop to proactively configure personalized products. A real-world case study, i.e., configure personalized electric clippers via a web-based sustainable configuration platform, is performed to validate the applicability of the proposed framework in the CMP context. Moreover, extensive experiments on the case study dataset demonstrate the superiority of SXHAGE over the state-of-the-art algorithms, e.g., surpassing Deep Neighbor-Aware Embedding (DNENC) by 18 % in F1-score for graph clustering and by 5 % in ROC-AUC for link prediction.