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Graph neural networks (GNNs) typically exhibit superior performance in shallow architectures. However, as the network depth increases, issues such as overfitting and oversmoothing of hidden vector representations arise, significantly diminishing model performance. To address these challenges, this paper proposes a Two-Channel Classification Algorithm Based on Graph Attention Network (TCC_GAT). Initially, nodes exhibiting similar interaction behaviors are identified through cosine similarity, thereby enhancing the foundational graph structure. Subsequently, an attention mechanism is employed to adaptively integrate neighborhood information within the enhanced graph structure, with a multi-head attention mechanism applied to mitigate overfitting. Furthermore, the K-nearest neighbors algorithm is adopted to reconstruct the basic graph structure, facilitating the learning of structural information and neighborhood features that are challenging to capture on interaction graphs. This approach addresses the difficulties associated with learning high-order neighborhood information. Finally, the embedding representations of identical nodes across different graph structures are fused to optimize model classification performance, significantly enhancing node embedding representations and effectively alleviating the over-smoothing issue. Semi-supervised experiments and ablation studies conducted on the Cora, Citeseer, and Pubmed datasets reveal an accuracy improvement ranging from 1.4% to 4.5% compared to existing node classification algorithms. The experimental outcomes demonstrate that the proposed TCC_GAT achieves superior classification results in node classification tasks. [ABSTRACT FROM AUTHOR]

As open-source data becomes increasingly accessible, the cost of code plagiarism has decreased, significantly impacting the healthy development of the software industry. Addressing the limitation of existing plagiarism detection methods, which struggle to deeply mine the semantic and structural information of source code, leading to suboptimal semantic plagiarism detection results, this paper introduces a graph neural network-based code plagiarism detection method. This method uses graph neural networks to effectively represent the characteristics of source code, including semantic and structural information, and employs graph attention networks to enhance these features. Furthermore, it utilizes neural tensor networks to obtain similarity vectors between different source codes. Finally, a fully connected network calculates the similarity between different source codes. Meanwhile, the dropout mechanism is incorporated to balance neuron weights, optimize model design, and prevent overfitting. To validate the effectiveness of the proposed method, experiments were conducted on an OJ system dataset, and the results were compared with those of current popular detection methods. The experimental results demonstrate that the proposed method achieves better performance. [ABSTRACT FROM AUTHOR]

Conversational flow refers to the progression of a conversation, encompassing the arrangement of topics discussed and how responses are delivered. A smooth flow involves participants taking turns to speak and respond naturally and intuitively. Conversely, a more disjointed flow may entail prolonged pauses or difficulties establishing common ground. Numerous factors influence conversation flow, including the personalities of those involved, their familiarity with each other, and the contextual setting. A conversational graph pattern outlines how a conversation typically unfolds or the underlying structure it adheres to. It involves combining different sentence types, the sequential order of topics discussed, and the roles played by different individuals. Predicting subsequent sentences relies on predefined patterns, the context derived from prior conversation flow in the data, and the trained system. The accuracy of sentence predictions varies based on the probability of identifying sentences that fit the subsequent pattern. We employ the Graph Recurrent Neural Network with Attention (GRNNA) model to generate conversational graphs and perform next-sentence prediction. This model constructs a conversational graph using an adjacency matrix, node features (sentences), and edge features (semantic similarity between the sentences). The proposed approach leverages attention mechanisms, recurrent updates, and information aggregation from neighbouring nodes to predict the next node (sentence). The model achieves enhanced predictive capabilities by updating node representations through multiple iterations of message passing and recurrent updates. Experimental results using the conversation dataset demonstrate that the GRNNA model surpasses the Graph Neural Network (GNN) model in next-sentence prediction, achieving an impressive accuracy of 98.89%. [ABSTRACT FROM AUTHOR]

A graph neural network (GNN) is one of successful methods for handling tasks on a graph data structure, e.g. node embedding, link prediction and node classification. GNNs focus on a graph data structure that must aggregate messages on nodes in the graph to retain a graph-structured information in new node's message and proceed tasks on a graph. One of modifications on the propagation step in GNNs by adopting attention mechanism is a graph attention network (GAT). Applying this modification to signed graphs generated by sociological theories is called signed graph attention network (SiGAT). In this research, we utilize SiGAT and create novel graphs using graph characters to assess the performance of SiGAT models embedded in nodes across various characteristic graphs. The primary focus of our study was linked prediction, which aligns with the task employed in the previous research on SiGAT. We propose a method using graph characteristics to improve the time spent on the learning process in SiGAT. [ABSTRACT FROM AUTHOR]

Graph-structured data is ubiquitous in real-world applications, such as social networks, citation networks, and communication networks. Graph neural network (GNN) is the key to process them. In recent years, graph attention networks (GATs) have been proposed for node classification and achieved encouraging performance. It focuses on the content associated on nodes to evaluate the attention weights, and the rich structure information in the graph is almost ignored. Therefore, we propose a multi-head attention mechanism to fully employ node content and graph structure information. The core idea is to introduce the interactions in the topological structure into the existing GATs. This method can more accurately estimate the attention weights among nodes, thereby improving the convergence of GATs. Second, the mechanism is lightweight and efficient, requires no training to learn, can accurately analyze higher-order structural information, and can be strongly interpreted through heatmaps. We name the proposed model content- and structure-based graph attention network (CSGAT). Furthermore, our proposed model achieves state-of-the-art performance on a number of datasets in node classification. The code and data are available at https://github.com/CroakerShark/CSGAT. [ABSTRACT FROM AUTHOR]

Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients' profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features. [ABSTRACT FROM AUTHOR]

In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks. However, current research on graph neural networks lacks understanding of the high-order structural features of networks, focusing mostly on node features and first-order neighbor features. This article proposes two new models, MGAT and MGATv2, by introducing high-order structure motifs that frequently appear in networks and combining them with graph attention mechanisms. By introducing a mixed information matrix based on motifs, the generation process of graph attention coefficients is improved, allowing the model to capture higher-order structural features. Compared with the latest research on various graph neural networks, both MGAT and MGATv2 achieve good results in node classification tasks. Furthermore, through various experimental studies on real datasets, we demonstrate that the introduction of network structural motifs can effectively enhance the expressive power of graph neural networks, indicating that both high-order structural features and attribute features are important components of network feature learning. [ABSTRACT FROM AUTHOR]

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy. Current deep learning methods often fail to fully exploit the intrinsic interconnections within cells, resulting in unsatisfactory results. Given these obstacles, we propose a unique approach for analyzing scRNA-seq data called scMPN. This methodology integrates multi-layer perceptron and graph neural network, including attention network, to execute gene imputation and cell clustering tasks. In order to evaluate the gene imputation performance of scMPN, several metrics like cosine similarity, median L1 distance and root mean square error are used. These metrics are utilized to compare the efficacy of scMPN with other existing approaches. This research utilizes criteria such as adjusted mutual information, normalized mutual information and integrity score to assess the efficacy of cell clustering across different approaches. The superiority of scMPN over current single-cell data processing techniques in cell clustering and gene imputation investigations is shown by the experimental findings obtained from four datasets with gold-standard cell labels. This observation demonstrates the efficacy of our suggested methodology in using deep learning methodologies to enhance the interpretation of scRNA-seq data. [ABSTRACT FROM AUTHOR]

Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this article, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterward, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information, and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN. [ABSTRACT FROM AUTHOR]

Extracting social relationships between people from images has an important role in criminal investigation, privacy protection and other fields. Existing graph modeling approaches have achieved good results by creating interpersonal relationship graphs or constructing knowledge graphs to learn people's relationships. However, the methods based on graph convolutional neural network (GCN) ignore different degrees of importance of different features for specific relationships to some extent. In order to solve this problem, this paper proposed a graph attention-based double-branch social relationship recognition model(GAT-DBSR). The first branch extracting person regions as well as image global features as nodes, and the core updated these nodes to learn feature representations of person relationships through graph attention networks and gating mechanisms. The second branch extracted scene features by convolutional neural networks to enhance the recognition of relationships between people. Finally, it fused and classified the features of the two branches to obtain all social relationships. The model achieves an mAP of 74.4% on the fine-grained relationship recognition task on the PISC dataset, a 1.2% improvement compared to the baseline model. The accuracy of relationship recognition on the PIPA dataset also shows some improvement. The experimental results show that the model has better results. [ABSTRACT FROM AUTHOR]

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

Session-based recommendation algorithm is a research hotspot with economic significance and research value. Most of the algorithms are based on how to represent users and items better. Deep learning has made many achievements in the recommendation area due to its strong representation ability. Nevertheless, in recent years, the excellent performance of the graph neural network in network representation provides many inspirations. Numerous recommendation algorithms based on graph neural networks only consider building a graph for each session to handle recommendation tasks. The relationship between items in the entire data set is ignored. Therefore, we propose a recommendation algorithm called GS-GNN for integrating items' global features and local features by graph neural network. By modeling the entire data as a global graph, we use the graph attention network to learn global representations of the items. We model each session as a session graph and use a gated graph neural network to learn local representations of the items. Sessions' representations are obtained through the fusion of items. The task is to recommend top-k items for each session by items' and sessions' representations. We did a comparative experiment and performance analysis experiment. The comparative experiments prove the effectiveness of GS-GNN, and we also conduct a detailed analysis of the model through experiments. [ABSTRACT FROM AUTHOR]

In graph-structured data, the node content contains rich information. Therefore, how to effectively utilize the content is crucial to improve the performance of graph convolutional networks (GCNs) on various analytical tasks. However, current GCNs do not fully utilize the content, especially multi-order content. For example, graph attention networks (GATs) only focus on low-order content, while high-order content is completely ignored. To address this issue, we propose a novel graph attention network with adaptability that could fully utilize the features of multi-order content. Its core idea has the following novelties: First, we constructed a high-order content attention mechanism that could focus on high-order content to evaluate attention weights. Second, we propose a multi-order content attention mechanism that can fully utilize multi-order content, i.e., it combines the attention mechanisms of high- and low-order content. Furthermore, the mechanism has adaptability, i.e., it can perform a good trade-off between high- and low-order content according to the task requirements. Lastly, we applied this mechanism to constructing a graph attention network with structural symmetry. This mechanism could more reasonably evaluate the attention weights between nodes, thereby improving the convergence of the network. In addition, we conducted experiments on multiple datasets and compared the proposed model with state-of-the-art models in multiple dimensions. The results validate the feasibility and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research. [ABSTRACT FROM AUTHOR]

In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks. However, current research on graph neural networks lacks understanding of the high-order structural features of networks, focusing mostly on node features and first-order neighbor features. This article proposes two new models, MGAT and MGATv2, by introducing high-order structure motifs that frequently appear in networks and combining them with graph attention mechanisms. By introducing a mixed information matrix based on motifs, the generation process of graph attention coefficients is improved, allowing the model to capture higher-order structural features. Compared with the latest research on various graph neural networks, both MGAT and MGATv2 achieve good results in node classification tasks. Furthermore, through various experimental studies on real datasets, we demonstrate that the introduction of network structural motifs can effectively enhance the expressive power of graph neural networks, indicating that both high-order structural features and attribute features are important components of network feature learning.

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Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively. [ABSTRACT FROM AUTHOR]

Entity linking is a process of linking mentions in a document with entities in a knowledge base. Collective entity disambiguation refers to mapping of multiple mentions in a document with their corresponding entities in a knowledge base. Most previous research has been based on the assumption that all mentions in the same document represent the same topic. However, mentions usually correspond to different topics. In this article, we proposes a new global model to explore the extraction of multitopic coherence in the same document. Herein, we present mention association graphs and candidate entity association graphs to obtain multitopic coherence features of the same document using graph neural networks (GNNs). In particular, we propose a variant GNN for our model and a particular graph readout function. We conducted extensive experiments on several datasets to demonstrate the effectiveness to the proposed model. [ABSTRACT FROM AUTHOR]

In graph-structured data, the node content contains rich information. Therefore, how to effectively utilize the content is crucial to improve the performance of graph convolutional networks (GCNs) on various analytical tasks. However, current GCNs do not fully utilize the content, especially multi-order content. For example, graph attention networks (GATs) only focus on low-order content, while high-order content is completely ignored. To address this issue, we propose a novel graph attention network with adaptability that could fully utilize the features of multi-order content. Its core idea has the following novelties: First, we constructed a high-order content attention mechanism that could focus on high-order content to evaluate attention weights. Second, we propose a multi-order content attention mechanism that can fully utilize multi-order content, i.e., it combines the attention mechanisms of high- and low-order content. Furthermore, the mechanism has adaptability, i.e., it can perform a good trade-off between high- and low-order content according to the task requirements. Lastly, we applied this mechanism to constructing a graph attention network with structural symmetry. This mechanism could more reasonably evaluate the attention weights between nodes, thereby improving the convergence of the network. In addition, we conducted experiments on multiple datasets and compared the proposed model with state-of-the-art models in multiple dimensions. The results validate the feasibility and effectiveness of the proposed model.

Graph Neural Networks (GNNs) have gained widespread usage and achieved remarkable success in various real-world applications. Nevertheless, recent studies reveal the vulnerability of GNNs to graph adversarial attacks that fool them by modifying graph structure. This vulnerability undermines the robustness of GNNs and poses significant security and privacy risks across various applications. Hence, it is crucial to develop robust GNN models that can effectively defend against such attacks. One simple approach is to remodel the graph. However, most existing methods cannot fully preserve the similarity relationship among the original nodes while learning the node representation required for reweighting the edges. Furthermore, they lack supervision information regarding adversarial perturbations, hampering their ability to recognize adversarial edges. To address these limitations, we propose a novel Dual Robust Graph Neural Network (DualRGNN) against graph adversarial attacks. DualRGNN first incorporates a node-similarity-preserving graph refining (SPGR) module to prune and refine the graph based on the learned node representations, which contain the original nodes' similarity relationships, weakening the poisoning of graph adversarial attacks on graph data. DualRGNN then employs an adversarial-supervised graph attention (ASGAT) network to enhance the model's capability in identifying adversarial edges by treating these edges as supervised signals. Through extensive experiments conducted on four benchmark datasets, DualRGNN has demonstrated remarkable robustness against various graph adversarial attacks. [ABSTRACT FROM AUTHOR]

Drug–target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug–drug and target–target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs. [ABSTRACT FROM AUTHOR]

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665). [ABSTRACT FROM AUTHOR]

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.

Graph Neural Network (GNN) is a promising technique in representation learning on graph data. Graph Convolution Network (GCN) and Graph Attention Network (GAT) are two main representative techniques in GNN, which can learn node embedding on the graph by aggregating the embedding of the neighborhoods. However, the complex computation process of existing GCN and GAT makes them less effective for tasks with complex graph structures. Moreover, traditional GCN and GAT ignore the time factor in user representation learning, so the obtained user preference model is static and cannot reflect the dynamics of user preferences. In this work, we propose a time-aware Lightweight Graph Convolutional Attention Network (LightGCAN), which can capture static and dynamic user preferences efficiently by using different GNN strategies. Specifically, static user preferences are modeled by a GCN with only neighborhood aggregation, and dynamic user preferences are extracted by a time-aware GAT based on recently interacted items. Static and dynamic user preferences are then combined and fed into a dual-channel Deep Neural Network (DNN) model for feature interaction learning and matching score prediction. Extensive experiments on different datasets show that LightGCAN is superior to the SOTA recommendation method. [ABSTRACT FROM AUTHOR]

Lithology identification plays an important role in petroleum exploration. However, due to the high cost of labeling by cores and cuttings, the application of semi-supervised method in lithology identification should be considered. There is a main problem in the past graph-based semi-supervised methods, that is, the connection strengths (i.e., edges) among samples are calculated before training and therefore fixed. However, these pre-handcrafted connections strengths determine the graph structure and therefore affect the performance of the trained model. Obviously, there inevitably exist inappropriate connection settings, which motivates us to consider the dynamic adjustment of these connection strengths via training. First, the graph attention network is used to complete the semi-supervised task in the process of lithology identification. This is the first time to our knowledge that the graph attention network is applied to lithology identification. This method can learn edge weights and mitigate interference due to the error of initial edge weights. Second, two methods are used in graph neural network to establish the connection between logging data and transforming them into graph structure data. Third, through a lot of experiments, the graph attention network using depth similarity (GAT-D) outperforms other algorithms on two metrics (macro-avg-f1 score of 73.73% and macro-avg-recall of 80.54% in dataset A). Hence, converting logging data into graph data and then using graph neural network to realize semi-supervised classification could improve the performance of lithology identification. • This is the first time to our knowledge that GAT is applied to lithology identification. • Logging data is shifted into graph structure data using feature and depth similarities. • Substantial experiments proved that the proposed method is effective. [ABSTRACT FROM AUTHOR]