Aggregation Node Research Articles

Metrological parameter planning method based on a multi-head sparse graph attention network for airborne products

The airborne system plays a crucial role in upholding the operational capabilities of an aircraft, and the caliber of its products significantly influences the overall reliability and safety of the aircraft. The foundational task in guaranteeing product quality is metrology, wherein metrological parameters play a pivotal role in shaping the ultimate product quality and determining metrological efficiency. Because the metrology documents are scattered in various departments and the data are highly isolated, manual planning methods are highly subjective, resulting in error-prone and inefficient results. The existing parameter planning methods based on deep learning suffer from the problems of strong training data dependency and poor model interpretability. For this reason, this paper proposes a metrological parameter planning method based on a multi-head sparse graph attention network (MHSGAT) for airborne products. First, an attribute graph structure is designed to represent product metrology information, and a metrology parameter graph dataset is constructed based on historical documents. Then, the proposed MHSGAT assigns sparse attention coefficients to the neighbors of nodes for feature aggregation. Finally, the metrology parameter planning results are output in the dense layer. The experimental results show that the proposed method outperforms the other four baseline methods. The significance of the proposed method in enhancing the metrological accuracy of airborne products and ensuring their quality is demonstrated by application cases.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

Decentralized collaborative machine learning for protecting electricity data

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users’ power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users’ local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy.

A robust federated learning algorithm for partially trusted environments

Due to the distributed nature of federated learning, it is vulnerable to poisoning attacks during the training process. The model’s resistance to poisoning attacks can be improved using robust aggregation algorithms. Current research on federated learning to resist poisoning attacks is mainly based on two settings: No trust or Byzantine robustness. However, both settings are not close enough to reality in practical scenarios. In many practical applications, some participants in federated learning are trustworthy. For example, participants who have participated in the training of this model before and performed very well, or participants with strong compliance and credibility such as governments and some national agencies participate in the training. In existing research, these trusted participants still have to accept the judgment of the aggregation node, which generates unnecessary computation, increases overhead, and does not take advantage of a trusted environment. Since there is no attack behavior on the trusted client, its training results are used to classify the trustworthiness of other untrusted clients and identify attack nodes with higher accuracy. Therefore, this paper proposes a robust federated learning algorithm for partially trusted environments. The proposed scheme uses the experimental results of trusted clients to judge the behavior of untrustworthy clients by the cosine similarity and the Local Outlier Factor and further identify and detect malicious clients. Experiments are performed on MNIST and CIFAR datasets. Comparison with other six aggregation algorithms under 30% attack scenario. And compared with the other four aggregation algorithms under 70% attack conditions. Our algorithm is more accurate than almost all of the other aggregation algorithms. The paper is the first to conduct robust research on federated learning in a partially trusted environment, and the proposed algorithm can more effectively resist poisoning attacks.

B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain

We propose a novel decentralized federated learning framework called B2DFL. It decomposes the aggregation process of vanilla FL into layered and serialized sub-aggregation processes and offloads the communication and computation from a single point to distributed nodes, thus addressing the single point of failure issue in centralized FL. The decentralization of B2DFL is based on the Butterfly, a distributed network topology, to organize and orchestrate the order and rules of node aggregation. Additionally, to mitigate potential risks such as dropouts or tampering, we leverage the blockchain and IPFS systems. Specifically, after each node completes its computation (including training and aggregation), it generates a hash value of the results as proof. We maintain a Tamper-evident Data Structure (TDS) on the blockchain, which records these proofs to ensure tamper-proofing and fast verification. To reduce the storage burden on the blockchain and improve throughput, we store the aggregated results on IPFS, a system that enables quick data location through hash values of data, for data backup. We also design a node replacement mechanism for quick dropout handling. We conduct a comprehensive performance evaluation and experimental results demonstrate that B2DFL presents a significant performance improvement while achieving privacy and decentralization.

Adaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Network

Wireless Sensor Networks (WSN) use sensor nodes placed in potential places to collect sensitive information. These sensor nodes monitor necessary data and send it to the sink node. Sensor nodes have resource constraints, especially energy and power depletion. The majority of sensor and battery power is wasted on redundant data transmission. The redundant data transmission consumes a significant amount of the sensor and battery life, decreasing the total lifespan of the sensor nodes. Data aggregation is an approach that expands the useful lifetime of sensor nodes overall and removes unnecessary data and delays. There are many types of data aggregation techniques, such as centralized, tree-based, in-network, and cluster-based. The available tree-based data aggregation mechanism performs well, but the whole tree may be down due to single-node failure. Due to bottlenecks, node data aggregation suffers from an increased packet failure ratio. Another limitation is that every node aggregates data into slices, which consumes more energy. For this purpose, an adaptive and priority-based data aggregation and scheduling model (APB-DASM) for WSNs is proposed in this paper to address these issues. It is proposed that APB-DASM be used to improve quality of service (QoS) with regard to energy consumption and data transmission. The APB-DASM model aggregates sensor data into cluster heads and divides it into three formats: The first format categorizes the most important data that consists of four slices, and the second format categorizes the important data that consists of three slices. Format three data is represented in two slices, which is normal data. These three types of format data are aggregated on a priority basis, such that the highest priority is given to the first format, i.e., most important data; moderate priority is given to the second format, i.e., important data; and then low priority is given to the normal data. Due to an efficient priority-based data aggregation and scheduling algorithm, our proposed model sends the most important data first, and so on. Theoretical study and simulation research demonstrate that our proposed approach improves the existing tree-based models. By using APB-DASM, significant decreases in energy usage, packet delivery ratio, and overall QoS are achieved, and as a result, the WSNs' lifetime is thus increased. The proposed model is implemented in MATLAB, and the results are compared with existing tree-based models. Simulations comparing our model to the most recent models indicate that it worked effectively, reducing the packet failure ratio and energy usage by 36.8% and 30%, respectively, for CBF-ADA, D-SMART, and WDARS. This article emphasizes how the suggested methodology can be effectively used in the aggregation of coronavirus patient data. It demonstrates how adaptable and applicable our method is in the real world.

Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration.

Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.

Morphological examination of the visual system and orbital region in the red panda (Ailurus fulgens fulgens)

ObjectivesThe red panda is currently the only surviving member of the Ailuridae family in the Caniformia suborder. In this study, we provide data on anatomical, morphometric, histological and histochemical examination of the orbital region, eyelids, orbital gland, and eye tunics in two adult males Ailurus fulgens fulgens from the Wroclaw Zoological Garden, Poland.MethodsThe study involved morphometric analysis of the eyeball and selected accessory organs of the eye, along with analysis of the bony orbit, including its morphometry, macroscopic, and microscopic evaluation. Microscopic evaluation encompassed histological and histochemical staining, with the former involving hematoxylin & eosin (H&E), Movat pentachrome, picro-Mallory trichrome, Fontana-Masson, and the latter including PAS, AB pH 1.0, AB pH 2.5; AB pH 2.5/PAS, and HDI.ResultsThe upper (UE) and lower (LE) eyelids presented well-developed tarsal glands, sebaceous glands, and a characteristic simple alveolar gland (producing a mucous secretion). The palpebral part of the lacrimal gland was present. A single lymphoid follicle was observed only in the upper eyelids. The superficial gland of the third eyelid (SGTE) was a multilobar acinar complex that produces mucous secretion and is contained within the interlobular ducts of numerous aggregates of lymph nodes. The third eyelid (TE) was T-shaped and composed of hyaline tissue, containing CALT. The lacrimal gland (LG) also revealed a multilobar acinar complex that produced mucous secretion, with a single lymphoid follicle. The cornea consisted of 4 layers, as Bowman’s membrane was absent. The Vogt palisades composed of 7–10 layers of epithelial cells were demonstrated. The pupil was horizontally ovoid at rest (post-mortem). The sphincter pupil and the dilator pupil were well developed. Macroscopically, the tapetum lucidum appeared as a milky, non-opalescent crescent. Histologically, the choroidal tapetum lucidum cellulosum consisted of 5 to 9 layers of loosely packed oval cells. The retina showed a composition similar to that of terrestrial nocturnal carnivores.ConclusionsThe results of our research indicate that the anatomical features of the eye and orbital region in the red panda share similarities with those described in the Musteloidea clade, as well as the Canidae and Ursidae families.

GNN-MgrPool: Enhanced graph neural networks with multi-granularity pooling for graph classification

Graph neural networks (GNNs) have gained sufficient attention and are applied to various domain tasks. At present, numerous pooling approaches are being proposed to aggregate node features and obtain node embeddings. However, current GNNs are black-box models that typically use a flat or single pooling step to aggregate nodes, which only considers the similarity between nodes within the cluster. These approaches ignore the influence of relationships within and between clusters in the learning process. To address this issue, we propose a novel multi-granular pooling method that aggregates nodes by simultaneously considering density and relationships among nodes and clusters. This method allows us to obtain multi-granular node-embedding clusters from GNN layers. The clusters in the current layer are built upon those in the previous layer, and these clusters change from fine to coarse as the number of clusters decreases during learning, which is achieved by using multi-granular pooling (MgrPool). Additionally, there is an inclusion relation between adjacent layers, and the node representation of each layer is established through the ratio of node distance within clusters to that between clusters. Finally, we conducted several experiments on node and graph classification tasks by combining GNN models with this pooling approach. The results demonstrate that our GNN-MgrPool model outperforms similar state-of-the-art algorithms and largely improves the interpretability of the learning process.

Monitoring and management of green information in water ecological environment based on sensors and big data

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction in graph structures. In the pursuit of predicting gene expression, an endeavor shared by predecessors like the L1000 and Enformer methods, we introduce a novel spatial graph-neural network (GNN) approach. This innovative strategy incorporates graph features, encompassing both regulatory and structural elements. The regulatory elements include pair-wise gene correlation, biological pathways, protein-protein interaction networks, and transcription factor regulation. The spatial structural elements include chromosomal distance, histone modification and Hi-C inferred 3D genomic features. Principal Node Aggregation models, validated independently, emerge as frontrunners, demonstrating superior performance compared to traditional regression and other deep learning models. By embracing the spatial GNN paradigm, our method significantly advances the description of the intricate network of gene interactions, surpassing the performance, predictable scope, and initial requirements set by previous methods.

Visualization analysis of research progress and trends in coexistence of lung cancer and pulmonary tuberculosis using bibliometrics

AbstractBackgroundThe incidence of lung cancer combined with pulmonary tuberculosis has been increasing, but there is relatively limited published literature on the topic of lung cancer combined with tuberculosis (LC‐PTB) from a bibliometric perspective. Therefore, in this study, we aimed to quantitatively analyze the LC‐PTB‐related literature to better understand the current status of this field and identify future research trends.MethodsWe searched for articles related to LC‐PTB using the Web of Science Core Collection (SCI‐E) and conducted a visual analysis of publication quantity, countries, institutions, authors, journals, references, and keywords using bibliometric software (CiteSpace, VOSviewer, and Scimago Graphica).ResultsAs of January 8, 2024, a total of 460 publications related to LC‐PTB were included for analysis from 3705 retrieved records. The number of publications has been increasing almost yearly, with most from China (n = 123), followed by the United States (n = 77). Taipei Medical University contributed the most publications (n = 11). Jing‐Yang Huang and Yung‐Po Liaw (eight documents each) ranked first among the included authors. The Journal of Thoracic Oncology was the most productive academic journal on LC‐PTB. The aggregation of key nodes in the co‐citation network and the chronological sequence indicated that LC‐PTB research has shifted from initial hotspots such as lung diseases, bronchitis, and exposure to recent areas, including immunotherapy, immune checkpoint inhibitors, and nivolumab.ConclusionIn this study, we visualized the current status of LC‐PTB research as well as future trends using bibliometric methods, providing new insights into the differential diagnosis of LC‐PTB and its related promoting mechanisms.

Multi-source data integration for explainable miRNA-driven drug discovery

Explainable drug discovery driven by miRNA offers substantial application potential due to the high targetability of miRNAs. Despite their effectiveness, the current graph neural network (GNN)-based methods encounters three significant challenges. Initially, GNN-based prediction models, disseminating messages throughout the miRNA–disease graph, are susceptible to “over-smoothing”. Secondly, these approaches frequently pinpoint potential miRNA drugs through established miRNA–disease associations (MDAs), neglecting the possible interdependence between unvalidated miRNAs and diseases. Thirdly, the lack of interpretability in these models, coupled with their dependence on specific dataset training, leads to suboptimal generalization capabilities. We present MS-EMD, an explainable model for miRNA-driven drug discovery that integrates subgraph estimation with an energy-constrained diffusion approach. This model aims to accurately forecast potential miRNA drugs by analyzing data from both local and global viewpoints. The MS-EMD model utilizes subgraph estimation to aggregate and update node representations using local subgraphs, thereby preventing over-smoothing. Simultaneously, it unveils potential miRNA–disease dependencies through a global attention mechanism driven by an energy-constrained diffusion process, improving model interpretability. Furthermore, we employ a data fusion technology that integrates diverse similarity data to strengthen the initial representations of miRNAs and diseases. Through various comparison and ablation studies, we verified the MS-EMD model’s efficiency and stability, highlighting its potential as an explainable tool for miRNA-driven drug discovery. Our code and data are available at: https://github.com/lizhen5000/MS-EMD.git.

An IoT-based packet aggregation mechanism for the SDN-based wide area networks

The Internet of Things (IoT) is destined to play a crucial role in our daily lives. Numerous short packets are sent over the networks due to these IoT devices' communication with one another or with the relevant servers via the Internet, which causes an increase in the bps and pps in the network nodes, high overheads, and inefficient performance of the networks. Many packet aggregation mechanisms have been developed and studied to solve these issues. Among them, some works are about the aggregation of the IoT packets in the WAN area; however, they have few studies and discussions about selecting the aggregator nodes and the possible options. In this work, a packet aggregation mechanism is proposed for near-the-edge SDN-based AS networks in the WAN area, which can select the aggregation type and the aggregator nodes among the possible options. The simulation results of the proposed mechanism are presented for different options, and the related discussions and comparisons are made. The results show improvements in the network efficiency for different modes of the proposed packet aggregation mechanism related to the non-aggregation mode and simple/app-level aggregation in all network nodes.

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Development of Modified ECC-Based Secured FoG-Assisted Healthcare Data Management System in IoT-Enabled WSN

The Internet of Things (IoT) consists of a massive count of connected devices with different sensing support, particularly in medical health constraints sensing. In these summaries, secure communication and data collection to centralized servers is rather difficult for preventing the occurrence of diverse attacks for illegal data access. To tackle these problems, this task plans to design and implement the FoG-based secure data management system in IoT-WSN using adaptive encryption and decryption processes. As the FoG server is away from the corresponding aggregator node, it is transmitted via the other nodes using the proposed modified Elliptic Curve Cryptography (ECC)-based encryption process. For secured data transmission the designed method undergoes two main stages as message receiving stage, and the message-extraction stage. The hybridized meta-heuristic algorithm termed Jaya-Galactic Swarm Optimization (J-GSO) is used for modifying the ECC with an optimized key. From the simulation findings, the performance of the suggested scheme at the data size of three bytes is 3.08%, 3.22%, 3.65%, and 3.33% better than the MFO-m-ECC, PSO-m-ECC, GSO-m-ECC, and JA-m-ECC at the ECC curve variation of “secp192r1”. Experimental results reveal the superiority of the designed method when tested with baseline schemes regarding time complexity, space complexity, and cost function.

High-Performance Computing Web Search System Based on Computer Big Data

File sharing, streaming media, collaborative computing, and other P2P systems are all unicast to establish the corresponding overlapping network. The superimposed network is generally carried out based on the existing primary network. In this way, the access of each node is random. At the same time, this will cause the topological structure of the upper and lower layers to be inconsistent. This will increase the communication delay between nodes and cause an excellent bandwidth burden to the underlying network. The existing topology matching methods still face problems, such as poor scalability and long node aggregation time. This paper aims to design a topological distributed node aggregation method based on network coordination and distributed hash table (DHT) algorithm. This paper established a two-dimensional mesh model of nodes based on equal-distance concentric circles and divided into two equal areas. The parts of multiple namespaces correspond one by one according to their location. Because nodes are kept close, neighbours can be aggregated through DHT's primary "publish" and "search" primitives. Experimental results show that the TANRA method can match the network's topology under a slight delay and a large number of nodes. The TANRA method can effectively reduce the path delay in structured networks.

SGT++: Improved Scene Graph-Guided Transformer for Surgical Report Generation.

Automatically recording surgical procedures and generating surgical reports are crucial for alleviating surgeons' workload and enabling them to concentrate more on the operations. Despite some achievements, there still exist several issues for the previous works: 1) failure to model the interactive relationship between surgical instruments and tissue; and 2) neglect of fine-grained differences within different surgical images in the same surgery. To address these two issues, we propose an improved scene graph-guided Transformer, also named by SGT++, to generate more accurate surgical report, in which the complex interactions between surgical instruments and tissue are learnt from both explicit and implicit perspectives. Specifically, to facilitate the understanding of the surgical scene graph under a graph learning framework, a simple yet effective approach is proposed for homogenizing the input heterogeneous scene graph. For the homogeneous scene graph that contains explicit structured and fine-grained semantic relationships, we design an attention-induced graph transformer for node aggregation via an explicit relation-aware encoder. In addition, to characterize the implicit relationships about the instrument, tissue, and the interaction between them, the implicit relational attention is proposed to take full advantage of the prior knowledge from the interactional prototype memory. With the learnt explicit and implicit relation-aware representations, they are then coalesced to obtain the fused relation-aware representations contributing to generating reports. Some comprehensive experiments on two surgical datasets show that the proposed STG++ model achieves state-of-the-art results.

Graph convolutional collaborative filtering recommendation method based on temporal information during node aggregation process

Graph Convolutional Networks (GCN) are prevalent techniques in collaborative filtering recommendations. However, current GCN-based approaches for collaborative filtering recommendation have limitations in effectively embedding neighboring nodes during node and neighbor information aggregation. Furthermore, weight allocation for the user (or item) representations after convolution of each layer is too uniform. To resolve these limitations, we propose a new Graph Convolutional Collaborative Filtering recommendation method based on temporal information during the node aggregation process (TA-GCCF). The method aggregates and propagates information using Gated Recurrent Units, while dynamically updating features based on the timing and sequence of interactions between nodes and their neighbors. Concurrently, we have developed a convolution attention coefficient to ascertain the significance of embedding at distinct layers. Experiments on three benchmark datasets show that our method significantly outperforms the comparison methods in the accuracy of prediction.

A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks

In recent years, graph neural networks (GNNs) have been widely applied in recommender systems. However, existing recommendation algorithms based on GNNs still face challenges in node aggregation and feature extraction processes because they often lack the ability to capture the interactions between users and items, as well as users’ multiple intentions. This hinders accurate understanding of users’ needs. To address the aforementioned issues, we propose a recommendation model called multi-intent-aware interactive graph convolutional network (Multi-IAIGCN). This model is capable of integrating multiple user intents and adopts an interactive convolution approach to better capture the information on the interaction between users and items. First, before the interaction between users and items begins, user intents are divided and mapped into a graph. Next, interactive convolutions are applied to the user and item trees. Finally, by aggregating different features of user intents, predictions of user preferences are made. Extensive experiments on three publicly available datasets demonstrate that Multi-IAIGCN outperforms existing state-of-the-art methods or can achieve results comparable to those of existing state-of-the-art methods in terms of recall and NDCG, thus verifying the effectiveness of Multi-IAIGCN.