Graph Structure Features Research Articles

Hierarchical feature aggregation with mixed attention mechanism for single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) analysis is capable of elucidating cell heterogeneity and diversity, making cell-level biological research possible. Cell type clusterization is one of the main goals of scRNA-seq analysis. However, existing methods lack a flexible aggregation mechanism to fuse node attribute features and graph structural features adaptively. They also ignore multi-scale information embedded in different layers of networks. Here, we propose AGAC (Attention-driven Graph Attentional Clustering), a unified computational framework that applies hierarchical feature aggregation with mixed attentional mechanisms to scRNA-seq data clustering. Firstly, AGAC learns the attribute features of cells through graph attention autoencoder and the graph structural features among cells through graph neural network (GNN) respectively. Secondly, AGAC utilizes the heterogeneous intelligent fusion module to fuse these two kinds of features dynamically, as well as the multi-scale intelligent fusion module to aggregate the multi-scale information embedded in different layers of the GNN adaptively. In addition, AGAC adopts a dual self-supervision module for end-to-end training and synchronous optimization. Experimental results on 13 real scRNA-seq datasets demonstrate that AGAC is more effective than existing baseline methods because it takes into account the discriminative information in the network comprehensively and generates clustering results directly. The source code of AGAC can be downloaded at https://github.com/zzyqh/AGAC.

Superpixel perception graph neural network for intelligent defect detection of aero-engine blade

Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology which is borescope inspection is labor-intensive, time-consuming, and experience-dependent. To endow this technology with intelligence, a novel superpixel perception graph neural network (SPGNN) is proposed by utilizing a multi-stage graph convolutional network (MSGCN) for feature extraction and superpixel perception region proposal network (SPRPN) for region proposal. First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations. Then, MSGCN composed of several GCN blocks extracts graph structure features and performs graph information processing at graph level. Last but not least, the SPRPN is proposed to generate perceptual bounding boxes by fusing graph representation features and superpixel perception features. Therefore, the proposed SPGNN always implements feature extraction and information transmission at the graph level in the whole SPGNN pipeline, to alleviate the reduction of receptive field and information loss. To verify the effectiveness of SPGNN, we construct a simulated blade dataset with 3000 images. A public aluminum dataset is also used to validate the performances of different methods. The experimental results demonstrate that the proposed SPGNN has superior performance compared with the state-of-the-art methods.

Distance Enhanced Hypergraph Learning for Dynamic Node Classification

Dynamic node classification aims to predict the labels of nodes in the dynamic networks. Existing methods primarily utilize the graph neural networks to acquire the node features and original graph structure features. However, these approaches ignore the high-order relationships between nodes and may lead to the over-smoothing issue. To address these issues, we propose a distance enhanced hypergraph learning (DEHL) method for dynamic node classification. Specifically, we first propose a time-adaptive pre-training component to generate the time-aware representations of each node. Then we utilize a dual-channel convolution module to construct the local and global hypergraphs which contain the corresponding local and global high-order relationships. Moreover, we adopt the K-nearest neighbor algorithm to construct the global hypergraph in the embedding space. After that, we adopt the node convolution and hyperedge convolution to aggregate the features of neighbors on the hypergraphs to the target node. Finally, we combine the temporal representations and the distance enhanced representations of the target node to predict its label. In addition, we conduct extensive experiments on two public dynamic graph datasets, i.e., Wikipedia and Reddit. The experimental results show that DEHL outperforms the state-of-the-art baselines in terms of AUC.

Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.

SeDPGK: Semi-supervised software defect prediction with graph representation learning and knowledge distillation

Context:Constructing an effective defect prediction model relies on a substantial number of labeled program modules. Unfortunately, program module labeling is often time-consuming and error-prone. Semi-supervised software defect prediction (SSDP) can alleviate this issue by incorporating some labeled modules and the remaining unlabeled modules from the same project. Objective:However, previous SSDP methods ignore the significant influence of dependencies between software modules. The potential of knowledge distillation in leveraging labeled instances to guide the learning process and effectively utilizing information from unlabeled instances to improve SSDP performance has not been fully investigated. Method:We propose a novel approach SeDPGK. Specifically, to exploit the graph-structured knowledge, we first construct the program dependence graph to extract control and data dependencies among modules. Then we use graph neural networks (GNNs) to learn the graph representation of the module relationships and encode with the statement semantics of abstract syntax tree and traditional static features for diversity. Second, we integrate multiple GNNs jointly trained as teacher models to ensemble various styles of graph-based networks and generate trustworthy labels for unlabeled modules. Further, to preserve the teacher model’s sufficient structure and semantic knowledge, we adopt a trainable label propagation and multi-layer perception as the student model and mitigate the differences between the teacher and student models using two widespread knowledge distillation functions. Results:We conducted our experiments on 17 real-world projects. The experimental results show that SeDPGK outperforms semi-supervised baselines with an average improvement of 16.9% for PD, 42.5% for FAR, and 8.9% for AUC, respectively. Moreover, the performance improvement is consistently significant across multiple statistical tests. Conclusion:The effectiveness of SeDPGK comes from the aggregation of the different GNNs with heterogeneity. Moreover, the graph structure and semantic features hidden behind the source code play a crucial role in the distillation framework.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression.

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

Towards Continual Knowledge Graph Embedding via Incremental Distillation

Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge graph embedding (CKGE) task has been proposed to train the KGE model by learning emerging knowledge efficiently while simultaneously preserving decent old knowledge. However, the explicit graph structure in KGs, which is critical for the above goal, has been heavily ignored by existing CKGE methods. On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs. On the other hand, old triples are preserved with equal priority, failing to alleviate catastrophic forgetting effectively. In this paper, we propose a competitive method for CKGE based on incremental distillation (IncDE), which considers the full use of the explicit graph structure in KGs. First, to optimize the learning order, we introduce a hierarchical strategy, ranking new triples for layer-by-layer learning. By employing the inter- and intra-hierarchical orders together, new triples are grouped into layers based on the graph structure features. Secondly, to preserve the old knowledge effectively, we devise a novel incremental distillation mechanism, which facilitates the seamless transfer of entity representations from the previous layer to the next one, promoting old knowledge preservation. Finally, we adopt a two-stage training paradigm to avoid the over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of IncDE over state-of-the-art baselines. Notably, the incremental distillation mechanism contributes to improvements of 0.2%-6.5% in the mean reciprocal rank (MRR) score. More exploratory experiments validate the effectiveness of IncDE in proficiently learning new knowledge while preserving old knowledge across all time steps.

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

Multi-Channel Graph Convolutional Networks for Graphs with Inconsistent Structures and Features

Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features across the graph structure, which implicitly assumes that the graph structure and node features are consistent, i.e., they carry related information. However, in many real-world networks, node features may unexpectedly mismatch with the structural information. Existing GCNs fail to generalize to inconsistent scenarios and are even outperformed by models that ignore the graph structure or node features. To address this problem, we investigate how to extract representations from both the graph structure and node features. Consequently, we propose the multi-channel graph convolutional network (MCGCN) for graphs with inconsistent structures and features. Specifically, the MCGCN encodes the graph structure and node features using two specific convolution channels to extract two separate specific representations. Additionally, two joint convolution channels are constructed to extract the common information shared by the graph structure and node features. Finally, an attention mechanism is utilized to adaptively learn the importance weights of these channels under the guidance of the node classification task. In this way, our model can handle both consistent and inconsistent scenarios. Extensive experiments on both synthetic and real-world datasets for node classification and recommendation tasks show that our methods, MCGCN-A and MCGCN-I, achieve the best performance on seven out of eight datasets and the second-best performance on the remaining dataset. For simpler graph structures or tasks where the overhead of multiple convolution channels is not justified, traditional single-channel GCN models might be more efficient.

Leverage Variational Graph Representation for Model Poisoning on Federated Learning.

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location.

Graph theory-based techniques have recently been adopted for anomaly detection in hyperspectral images (HSIs). However, these methods rely excessively on the relational structure within the constructed graphs and tend to downplay the importance of spectral features in the original HSI. To address this issue, we introduce graph frequency analysis to hyperspectral anomaly detection (HAD), which can serve as a natural tool for integrating graph structure and spectral features. We treat anomaly detection as a problem of graph frequency location, achieved by constructing a beta distribution-based graph wavelet space, where the optimal wavelet can be identified adaptively for anomaly detection. Initially, a high-dimensional, undirected, unweighted graph is built using the pixels in the HSI as vertices. By leveraging the observation of energy shifting to higher frequencies caused by anomalies, we can dynamically pinpoint the specific Beta wavelet associated with the anomalies' high-frequency content to accurately extract anomalies in the context of HSIs. Furthermore, we introduce a novel entropy definition to address the frequency location problem in an adaptive manner. Experimental results from seven real HSIs validate the remarkable detection performance of our newly proposed approach when compared to various state-of-the-art anomaly detection methods.

Multi-Scale FC-Based Multi-Order GCN: A Novel Model for Predicting Individual Behavior From fMRI.

Predicting individual behavior from brain imaging data using machine learning is a rapidly growing field in neuroscience. Functional connectivity (FC), which captures interactions between different brain regions, contains valuable information about the organization of the brain and is considered a crucial feature for modeling human behavior. Graph convolutional networks (GCN) have proven to be a powerful tool for extracting graph structure features and have shown promising results in various FC-based classification tasks, such as disease classification and prognosis prediction. Despite this success, few behavior prediction models currently exist based on GCN, and their performance is not satisfactory. To address this gap, a new model called the Multi-Scale FC-based Multi-Order GCN (MSFC-MO-GCN) was proposed in this paper. The model considers the hierarchical structure of the brain system and utilizes FCs inferred from multiple spatial scales as input to comprehensively characterize individual brain organization. To enhance the feature learning ability of GCN, a multi-order graph convolutional layer is incorporated, which uses multi-order neighbors to guide message passing and learns high-order graph information of nodal connections. Additionally, an inter-subject contrast constraint is designed to control the potential information redundancy of FCs among different spatial scales during the feature learning process. Experimental evaluation were conducted on the publicly available dataset from human connectome project. A total of 805 healthy subjects were included and 5 representative behavior metrics were used. The experimental results show that our proposed method outperforms the existing behavior prediction models in all behavior prediction tasks.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

Retracted: Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Retracted: Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Adaptive Graph Convolution Methods for Attributed Graph Clustering

Attributed graph clustering is a challenging task as it requires to jointly model graph structure and node attributes. Although recent advances in graph convolutional networks have shown the effectiveness of graph convolution in combining structural and content information, there is limited understanding of how to properly apply it for attributed graph clustering. Previous methods commonly use a fixed and low order graph convolution, which only aggregates information of few-hop neighbours and hence cannot fully capture the cluster structures of diverse graphs. In this paper, we first propose an adaptive graph convolution method (AGC) for attributed graph clustering, which exploits high-order graph convolutions to capture global cluster structures and adaptively selects an appropriate order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> via intra-cluster distance. While AGC can find a reasonable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> and avoid over-smoothing, it is not sensitive to the gradual decline of clustering performance as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> increases. To search for a better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> , we further propose an improved adaptive graph convolution method (IAGC) that not only observes the variation of intra-cluster distance, but also considers the inconsistencies of filtered features with graph structure and raw features, respectively. We establish the validity of our methods by theoretical analysis and extensive experiments on various benchmark datasets.

An enhanced spatio-temporal constraints network for anomaly detection in multivariate time series

Anomaly detection using multivariate time series plays a crucial role in system security. Conventional deep learning detection techniques mainly depend on temporal dependency and employ reconstruction or prediction-based methods. However, as feature variables grow more intricate, there is a risk of neglecting essential spatio-temporal structural information, potentially leading to insufficient model training in unsupervised settings. Hence, we propose an end-to-end anomaly detection model with multiple pre-training tasks designed for the spatio-temporal dimension to enhance our constraints. Specifically, in the temporal dimension, we employ an autoregressive task to train timestamp associations using data’s concealed autocorrelation and periodicity. In the spatio dimension, we acquire knowledge of a diverse feature-related heterogeneous graph. Subsequently, we design three different graph contrastive learning tasks to tap into the effective information arising from the inherent heterogeneity and hierarchy in spatio structures. Through joint spatio-temporal modeling, we can effectively capture inter and intra-feature associations from series and graph structural features, enhancing model robustness to cope with the complex chain reactions between features. Finally, we assess our model on three real-world datasets: SWaT, WADI(2017, 2019), our F1 scores demonstrate enhancements of 6.17%, 18.3% and 5.35% over the top-tier baseline performance. Our model is applicable for both temporal and graph, is self-supervised learning for sparse data which is suitable for data sparsity and complex scenarios that need to capture spatio-temporal characteristics at the same time, for example, traffic flow detection and anomaly detection of intelligent systems. Further visualization experiments and case studies will provide a better interpretation of our model.

GFNet: A pioneering approach for precisely estimating ash content in coal through the fusion of graph convolution and feedforward network

Accurate determination of ash content in coal is paramount for quality control in the coal industry. However, conventional methods are time-consuming and ill-suited for real-time process control. To address this challenge, our study proposed a novel approach, GFNet, which integrated graph convolution and a feedforward network for ash content estimation. The image was divided into patches and transformed into a graph structure using a k-nearest Neighbor (k-NN) function. We introduced relative position encoding to each node feature to enhance feature representation. The GFNet architecture consisted of a stack of GF (graph convolution and FFN), facilitating feature extraction from the graph representation of image patches. Graph convolution captured intricate spatial relationships and interdependencies among patches, enabling effective information aggregation and updating within the graph. The FFN module, featuring two linear layers, facilitated node feature transformation. We developed pyramid (PGFNet) and isotropic (IGFNet) architectures by stacking GF blocks. Extensive experiments substantiated the superior performance of the proposed approach compared to traditional methods, emphasizing accuracy and efficiency. The GFNet achieved remarkable results, with PGFNet-S attaining a 96.36 accuracy and a mean absolute error (MAE) of 0.145%. IGFNet-Ti demonstrated a higher accuracy of 98.30% with an MAE of 0.093%. Visualization of the graph structure elucidated the model’s proficiency in processing graph structure and node features, illustrating its capability to capture content-relevant nodes and gradually connect nodes with similar semantics. This pioneering work presented a transformative approach to ash content estimation in coal quality analysis, offering an invaluable tool for the coal industry’s quality control needs.

Struct2GO: protein function prediction based on Graph pooling algorithm and AlphaFold2 structure information.

In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein-protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein-protein interaction network, such models cannot make effective predictions. To address this, this paper proposes the Struct2GO model, which combines protein structure and sequence data to enhance the precision of protein function prediction and the generality of the model. We obtain amino acid residue embeddings in protein structure through graph representation learning, utilize the graph pooling algorithm based on a self-attention mechanism to obtain the whole graph structure features, and fuse them with sequence features obtained from the protein language model. The results demonstrate that compared with the traditional protein sequence-based function prediction model, the Struct2GO model achieves better results. https://github.com/lyjps/Struct2GO. Supplementary data are available at Bioinformatics online.

Graph fairing convolutional networks for anomaly detection

Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations. This propagation rule is derived from the iterative solution of the implicit fairing equation via the Jacobi method. In addition to capturing information from distant graph nodes through skip connections between the network’s layers, our approach exploits both the graph structure and node features for learning discriminative node representations. These skip connections are integrated by design in our proposed network architecture. The effectiveness of our model is demonstrated through extensive experiments on five benchmark datasets, achieving better or comparable anomaly detection results against strong baseline methods. We also demonstrate through an ablation study that skip connection helps improve the model performance.