Graph Classification Tasks Research Articles

A Method for Single-Phase Ground Fault Section Location in Distribution Networks Based on Improved Empirical Wavelet Transform and Graph Isomorphic Networks

When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks based on improved empirical wavelet transform (IEWT) and GINs to address this issue. Firstly, based on kurtosis, EWT is optimized using the N-point search method to decompose the zero-sequence current signal into modal components. Noise is filtered out through weighted permutation entropy (WPE), and signal reconstruction is performed to obtain the denoised zero-sequence current signal. Subsequently, GINs are employed for graph classification tasks. According to the topology of the distribution network, the corresponding graph is constructed as the input to the GIN. The denoised zero-sequence current signal is the node input for the GIN. The GIN autonomously explores the features of each graph structure to achieve fault section location. The experimental results demonstrate that this method has strong noise resistance, with a fault section location accuracy of up to 99.95%, effectively completing fault section location in distribution networks.

Motif Graph Neural Network.

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose motif GNN (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations with respect to each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN uses an injective function to combine the representations with respect to different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both the node classification and graph classification tasks.

A Multichannel Convolutional Decoding Network for Graph Classification.

Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in decoding, resulting in the loss of global information or ignoring some local information of large graphs. And the commonly used cross-entropy loss is essentially an encoder-decoder global loss, which cannot supervise the training states of the two local components (encoder and decoder). We propose a multichannel convolutional decoding network (MCCD) to solve the above-mentioned problems. MCCD first adopts a multichannel GCN encoder, which has better generalization than a single-channel GCN encoder since different channels can extract graph information from different perspectives. Then, we propose a novel decoder with a global-to-local learning pattern to decode graph information, and this decoder can better extract global and local information. We also introduce a balanced regularization loss to supervise the training states of the encoder and decoder so that they are sufficiently trained. Experiments on standard datasets demonstrate the effectiveness of our MCCD in terms of accuracy, runtime, and computational complexity.

Generating graph perturbations to enhance the generalization of GNNs

Graph neural networks (GNNs) have become the standard approach for performing machine learning on graphs. Such models need large amounts of training data, however, in several graph classification and regression tasks, only limited training data is available. Unfortunately, due to the complex nature of graphs, common augmentation strategies employed in other settings, such as computer vision, do not apply to graphs. This work aims to improve the generalization ability of GNNs by increasing the size of the training set of a given problem. The new samples are generated using an iterative contrastive learning procedure that augments the dataset during the training, in a task-relevant approach, by manipulating the graph topology. The proposed approach is general, assumes no knowledge about the underlying architecture, and can thus be applied to any GNN. We provided a theoretical analysis regarding the equivalence of the proposed approach to a regularization technique. We demonstrate instances of our framework on popular GNNs, and evaluate them on several real-world benchmark graph classification datasets. The experimental results show that the proposed approach, in several cases, enhances the generalization of the underlying prediction models reaching in some datasets state-of-the-art performance.

Molecular Classification with Graph ConvolutionalNetworks: Exploring the MUTAG Dataset for Mutagenicity Prediction

This paper presents the implementation of a Graph Convolutional Network (GCN) for the classification of chemical compounds using the MUTAG dataset, which consists of 188 ni- troaromatic compounds labeled according to their mutagenicity. The GCN model leverages the inherent graph structure of molec-ular data to capture and learn from the relationships between atoms and bonds, represented as nodes and edges, respectively. By utilizing three graph convolutional layers followed by a global mean pooling layer, the model effectively aggregates node features to generate meaningful graph-level representations. The model was trained using the Adam optimizer with a learning rate of 0.01, and cross-entropy loss was employed to supervise the classification task. The results demonstrate the efficacy of GCNs in graph classification tasks, with the model achieving a training accuracy of 79.33% and a test accuracy of 76.32%. This study highlights the potential of GCNs in cheminformatics and other domains where graph-structured data is prevalent, paving the way for further exploration and application of advanced graph neural networks in similar tasks.

SPORT: A Subgraph Perspective on Graph Classification with Label Noise

Graph neural networks (GNNs) have achieved great success recently on graph classification tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels could exist in the real world because of the complicated processes of manual graph data annotations, which may significantly degrade the performance of GNNs. Therefore, we investigate the problem of graph classification with label noise, which is demanding because of the complex graph representation learning issue and serious memorization of noisy samples. In this work, we present a novel approach called S ubgra p h Set Netw or k with Sample Selection and Consis t ency Learning (SPORT) for this problem. To release the overfitting of GNNs, SPORT proposes to characterize each graph as a set of subgraphs generated by certain predefined stratagems, which can be viewed as samples from its underlying semantic distribution in graph space. Then we develop an equivariant network to encode the subgraph set with the consideration of the symmetry group. To further release the influences of noisy examples, we leverage the predictions of subgraphs to measure the likelihood of a sample being clean or noisy, followed by effective label updating. In addition, we propose a joint loss to advance the model generalizability by introducing consistency regularization. Comprehensive experiments on a wide range of graph classification datasets demonstrate the effectiveness of our SPORT. Specifically, SPORT outperforms the most competing baseline by up to 6.4%.

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.

A class-aware representation refinement framework for graph classification

Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/graph pooling, and existing methods to address overfitting operate on each individual graph. This makes the graph representations learnt less effective in the downstream classification. In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification. CARE computes simple yet powerful class representations and injects them to steer the learning of graph representations towards better class separability. CARE is a plug-and-play framework that is highly flexible and able to incorporate arbitrary GNN backbones without significantly increasing the computational cost. We also theoretically prove that CARE has a better generalization upper bound than its GNN backbone through Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 11 well-known GNN backbones on 9 benchmark datasets validate the superiority and effectiveness of CARE over its GNN counterparts.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights. However, they may neglect valuable heterophilic information or extract heterophilic information ineffectively, which could cause poor performance of downstream tasks on heterophilic graphs, including node classification and graph classification, etc. Hence, a novel framework named GARN is proposed to effectively extract both homophilic and heterophilic information. First, we analyze the shortcomings of most GNNs in tackling heterophilic graphs from the perspective of graph spectral and spatial theory. Then, motivated by these analyses, a Graph Aggregating-Repelling Convolution (GARC) mechanism is designed with the objective of fusing both low-pass and high-pass graph filters. Technically, it learns positive attention weights as a low-pass filter to aggregate similar adjacent nodes, and learns negative attention weights as a high-pass filter to repel dissimilar adjacent nodes. A learnable integration weight is used to adaptively fuse these two filters and balance the proportion of the learned positive and negative weights, which could control our GARC to evolve into different types of graph filters and prevent it from over-relying on high intra-class similarity. Finally, a framework named GARN is established by simply stacking several layers of GARC to evaluate its graph representation learning ability on both the node classification and image-converted graph classification tasks. Extensive experiments conducted on multiple homophilic and heterophilic graphs and complex real-world image-converted graphs indicate the effectiveness of our proposed framework and mechanism over several representative GNN baselines.

Graph Contrastive Multi-view Learning: A Pre-training Framework for Graph Classification

Recent advancements in node and graph classification tasks can be attributed to the implementation of contrastive learning and similarity search. Despite considerable progress, these approaches present challenges. The integration of similarity search introduces an additional layer of complexity to the model. At the same time, applying contrastive learning to non-transferable domains or out-of-domain datasets results in less competitive outcomes. In this work, we propose maintaining domain specificity for these tasks, which has demonstrated the potential to improve performance by eliminating the need for additional similarity searches. We adopt a fraction of domain-specific datasets for pre-training purposes, generating augmented pairs that retain structural similarity to the original graph, thereby broadening the number of views. This strategy involves a comprehensive exploration of optimal augmentations to devise multi-view embeddings. An evaluation protocol, which focuses on error minimization, accuracy enhancement, and overfitting prevention, guides this process to learn inherent, transferable structural representations that span diverse datasets. We combine pre-trained embeddings and the source graph as a beneficial input, leveraging local and global graph information to enrich downstream tasks. Furthermore, to maximize the utility of negative samples in contrastive learning, we extend the training mechanism during the pre-training stage. Our method consistently outperforms comparative baseline approaches in comprehensive experiments conducted on benchmark graph datasets of varying sizes and characteristics, establishing new state-of-the-art results.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Accurate learning of graph representation with the consideration of fuzzy overlapping community

Graph classification task plays a crucial role in many practical applications, for which the model is required to be capable of learning an accurate representation with discriminative characteristics, but existing methods typically fail to exploit the most authentic substructures for better representation. Aiming at this limitation, a fuzzy overlapping community guided subgraph neural network is proposed in this paper to make the best use of graph latent topologies for fine-grained representation learning and discriminative feature extraction, namely FS-GRAL. Taking multi-functional elements and their preferences fully into consideration, FS-GRAL allows the nodes to appear in more than one subgraph with different contributions for message passing, so that the model is powerful enough in capturing those natural aggregations inherent in complex networks to achieve accurate representation learning. Meanwhile, on the basis of learned accurate representations, a two-level pooling strategy dominated by intra-subgraph node discarding is presented for feature extraction, which enables graph latent topology can be integrated into extracted discriminative features, and thus the coarsened graph is more representative for graph classification. Extensive experiments on real-world benchmarks demonstrate that our fuzzy overlapping community guided subgraph neural network is highly competitive in learning the accurate graph representation and its intra-subgraph dominated two-level pooling strategy promotes a more promising classification performance.

Module-based graph pooling for graph classification

Graph Neural Network (GNN) models are recently proposed to process the graph-structured data for the learning tasks on graphs, e.g., node classification, link prediction, and so on. This work focuses on the graph classification task, aiming to obtain the graph representation and predict the class label for a graph. Existing works proposed applying graph pooling to obtain graph embedding but still suffer from several issues. First, node embeddings are generated according to the topological information of the whole graph, but ignoring the local isomorphic substructures commonly seen in bioinformatics and chemistry. Another limitation arises when aggregating node embeddings. The hard assignment obtained through clustering algorithms, which rely on preset and fixed parameters instead of considering the graph’s properties adaptively, restricts the flexibility in handling graphs of varying scales. To address the above problems, a module-based graph pooling framework (MGPool) is proposed in this work. Inspired by the rules of bioinformatics, MGPool assumes that a graph consists of multiple modules (also known as sub structures), which are identified based on the natural organization of the graph rather than the hard allocation of nodes. Benefiting from the hypothesis, MGPool generates node embeddings from graph-view and module-view, which is capable to capture global graph information and local isomorphic information respectively. Then module-level pooling is used to capture the intra-module information, while the inter-module information in terms of the correlation between modules is obtained through graph-level pooling. Finally, an entropy-based weighting mechanism is proposed to adjust the modules’ weights for the graph aggregation. Experiments conducted on bioinformatics benchmark datasets demonstrate the effectiveness of MGPool by outperforming other state-of-the-art graph pooling methods. For social network datasets, MGPool also provides competitive performance. Moreover, the visualization of module entropy weights is given to reveal the interpretability of the model.

Fully Binarized Graph Convolutional Network Accelerator Based on In‐Memory Computing with Resistive Random‐Access Memory

Artificial intelligence for graph‐structured data has achieved remarkable success in applications such as recommendation systems, social networks, drug discovery, and circuit annotation. Graph convolutional networks (GCNs) are an effective way to learn representations of various graphs. The increasing size and complexity of graphs call for in‐memory computing (IMC) accelerators for GCN to alleviate massive data transmission between off‐chip memory and processing units. However, GCN implementation with IMC is challenging because of the large memory consumption, irregular memory access, and device nonidealities. Herein, a fully binarized GCN (BGCN) accelerator based on computational resistive random‐access memory (RRAM) through software–hardware codesign is presented. The essential operations including aggregation and combination in GCN are implemented on the RRAM crossbar arrays with cooperation between multiply‐and‐accumulation and content‐addressable memory operations. By leveraging the model quantization and IMC on the RRAM, the BGCN accelerator demonstrates less RRAM usage, high robustness to the device variations, high energy efficiency, and comparable classification accuracy compared to the current state‐of‐the‐art GCN accelerators on both graph classification task using the MUTAG and PTC datasets and node classification task using the Cora and CiteSeer datasets. These results provide a promising approach for edge intelligent systems to efficiently process graph‐structured data.

Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery

Abstract Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for capturing diverse pharmacological data for efficacy predictions, thanks to their ability to integrate multimodal data and represent relationships such as gene-gene or drug-target interactions as edges. They have also had proven success across a range of other drug discovery tasks including repositioning and target identification. In this study, we sought to address the explainability challenges of drug response predictions. Recent developments in the field of Graph AI have led to improvements in interpretability mechanisms that highlight parts of a graph which are driving predictions. We have conducted a comprehensive review of multiple major approaches for tackling drug efficacy prediction using graph methods, benchmarking the performance and interpretability of these algorithms across indications. Methods: We assembled a combined dataset of GDSC1 and GDSC2 drug response data in cell lines, with multiomic cell line data and drug target and chemical structure data. We then applied graph-based approaches for the prediction of binarized IC50 on an indication-by-indication basis. Approach 1 involved the creation of a ‘GDSC knowledge graph’, where drug response and cell line ‘omic information is represented in an unweighted knowledge graph: cell lines are connected to genes expressed in them, drugs are connected to genes they target, and so on. We then used state-of-the-art graph embedding techniques to predict IC50 using paired drug and cell line embeddings. In Approach 2 we used a weighted knowledge graph instead, and generated embeddings using heterogeneous graph neural networks (HGNNs). In Approach 3, we modelled response prediction as a graph classification task, where one single graph captures one drug-cell line interaction. The graph classifier and HGNN models both have in-built interpretability mechanisms, including graph attention, that can signify the genes in the cell line which were most important for the eventual prediction. We can also integrate biomedical prior knowledge with all these models by capturing gene-pathway and gene-gene data in the graphs. Results: Our models outperformed benchmark models including DNNs and GBMs, and identified both established and novel response biomarkers in NSCLC cell lines (AUC = 0.94, Accuracy = 89%). We have also applied our models to Breast Cancer, Pancreatic Cancer, Colorectal Cancer and Haematological malignancies with similar predictive performance and explainability. Conclusions:Our graph analytical framework for response predictions showed better performance than benchmarking models and provided insights from explainability. This framework is easily extendable to response and ‘omic data from any disease model and patient studies. Citation Format: Jake Cohen-Setton, Krishna Bulusu, Jonathan Dry, Ben Sidders. Explainable AI: Graph machine learning for response prediction and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 902.

GSEDroid: GNN-based Android malware detection framework using lightweight semantic embedding

Currently, the prevalence of Android malware remains substantial. Malicious programs increasingly use advanced obfuscation techniques, posing challenges for security professionals with enhanced disguises, a proliferation of variants, and escalating detection difficulty. Leveraging semantic features presents a promising avenue to address these challenges. Rich semantic information encapsulated within opcodes and API call graphs has been identified as crucial in distinguishing benign from malicious applications. Consequently, various Natural Language Processing (NLP) technologies, such as Word2vec, are employed to encode features of Dalvik opcode sequences, thereby yielding embedded representations. Given that malware developers often opt for semantically similar APIs to achieve comparable functionalities, it is posited that the opcode embeddings for such APIs should exhibit similar characteristics. However, simple NLP models that only extract statistical information are insufficient for understanding obfuscated malware's behavioral patterns, as they do not provide comprehensive semantic insights. To bridge this gap, we propose a novel, lightweight embedding model based on CodeBERT and TextCNN. This model aims for efficient and precise representation of opcode sequences. Consequently, we introduce GSEDroid, an Android malware detection framework that uses an API call graph with permission and opcode semantic features to characterize APKs. This approach converts the detection challenge into a graph classification task executed via a graph neural network algorithm. The efficacy of our method has been validated through comparative analyses with other techniques. Experimental results demonstrate that our GraphSage+SAGPooling model achieved an accuracy of 99.47% and an F1-score of 99.44%, underscoring its effectiveness in Android malware detection.

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records.

Text classification is a central part of natural language processing, with important applications in understanding the knowledge behind biomedical texts including electronic health records (EHR). In this article, we propose a novel heterogeneous graph convolutional network method for classifying EHR texts. Our method, called EHR-HGCN, is able to combine context-sensitive word and sentence embeddings with structural sentence-level and word-level relation information to perform text classification. EHR-HGCN reframes EHR text classification as a graph classification task to better capture structural information about the document using a heterogeneous graph. To mine contextual information from a document, EHR-HGCN first applies a bidirectional recurrent neural network (BiRNN) on word embeddings obtained via Global Vectors for word representation (GloVe) to obtain context-sensitive word-level and sentence-level embeddings. To mine structural relationships from the document, EHR-HGCN then constructs a heterogeneous graph over the word and sentence embeddings, where sentence-word and word-word relationships are represented by graph edges. Finally, a heterogeneous graph convolutional neural network is used to classify documents by their graph representation. We evaluate EHR-HGCN on a variety of standard text classification benchmarks and find that EHR-HGCN has higher accuracy and F1-score than other representative machine learning and deep learning methods. We also apply EHR-HGCN to the MedLit benchmark and find it performs with high accuracy and F1-score on the task of section classification in EHR texts. Our ablation experiments show that the heterogeneous graph construction and heterogeneous graph convolutional network are critical to the performance of EHR-HGCN.

GBCA: Graph Convolution Network and BERT combined with Co-Attention for fake news detection

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social stability. Current fake news detection methods primarily rely on graph neural network, which analyze the dissemination patterns of news articles. Nevertheless, these approaches frequently overlook the semantic characteristics of the news content itself. To address this problem, we propose a novel Graph Convolution Network and BERT combined with Co-Attention (GBCA) model. Initially, we conduct training for a graph classification task on the propagation structure of fake news. Subsequently, we employ the BERT model to extract semantic features in fake news. Finally, we utilize co-attention mechanism to integrate the two dimensions of propagation structure and semantic features, which enhances the effectiveness of fake news detection. Our model outperforms baseline methods in terms of accuracy and training time, as demonstrated by experiments on three public benchmark datasets.

FAGnet: Family-aware-based android malware analysis using graph neural network

Android malware family analysis is essential for building an efficient malware detection mechanism. In recent years, many graph representation learning-based malware detection and classification studies have been proposed, and many methods model malware as graph data to mine the behavioral semantics of malware. However, they do not consider the relationship at the sample (graph) level, and malware belonging to the same family has similar malicious behavior. The transformation of samples according to the Data Processing Inequality (DPI) will lead to the loss of mutual information transmission, which inspired us to consider the analysis of malware based on graph representation learning from this perspective. In this paper, we consider introducing the relationship between malware samples, inserting a family representation refinement component that is conducive to improving the family separability in the graph classification task, and propose a Family-Aware Graph neural network Android malware analysis (FAGnet). We use 4 backbones to perform extension experiments on 2 benchmark datasets and comprehensively compare some baseline methods. The experiments verify the effectiveness of FAGnet, which achieves 98.11 % accuracy on the Drebin dataset and 83.45 % and 72.76 % accuracy on the CICAndMal2017 category and family classification, respectively. In addition, FAGnet is evaluated with real-world data, and its satisfactory performance was maintained in real-world scenarios.

Graph Domain Adaptation: A Generative View

Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tasks with limited samples. However, current graph domain adaptation methods are generally adopted from traditional domain adaptation tasks, and the properties of graph-structured data are not well utilized. For example, the observed social networks on different platforms are controlled not only by the different crowds or communities but also by domain-specific policies and background noise. Based on these properties in graph-structured data, we first assume that the graph-structured data generation process is controlled by three independent types of latent variables, i.e., the semantic latent variables, the domain latent variables, and the random latent variables. Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules. Extensive experimental results on two real-world datasets in the graph classification task reveal that our method not only significantly outperforms the traditional domain adaptation methods and the disentangled-based domain adaptation methods but also outperforms the state-of-the-art graph domain adaptation algorithms. The code is available at https://github.com/rynewu224/GraphDA .