Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated their vulnerability to potentially existent adversarial examples on graph-structured data. Existing approaches are either limited to structure attacks or restricted to local information, urging for the design of a more general attack framework on graph classification, which faces significant challenges due to the complexity of generating local-node-level adversarial examples using the global-graph-level information. To address this "global-to-local" attack challenge, we present a novel and general framework to generate adversarial examples via manipulating graph structure and node features. Specifically, we make use of Graph Class Activation Mapping and its variant to produce node-level importance corresponding to the graph classification task. Then through a heuristic design of algorithms, we can perform both feature and structure attacks under unnoticeable perturbation budgets with the help of both node-level and subgraph-level importance. Experiments towards attacking four state-of-the-art graph classification models on six real-world benchmarks verify the flexibility and effectiveness of our framework.Comment: 13 pages, 7 figure

Adversarially Robust Neural Architecture Search for Graph Neural Networks

Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks.Comment: Accepted as a conference paper at CVPR 202

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.Comment: icd

Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation

Recently, domain adaptation based on deep models has been a promising way to deal with the domains with scarce labeled data, which is a critical problem for deep learning models. Domain adaptation propagates the knowledge from a source domain with rich information to the target domain. In reality, the source and target domains are mostly unbalanced in that the source domain is more resource-rich and thus has more reliable knowledge than the target domain. However, existing deep domain adaptation approaches often pre-assume the source and target domains balanced and equally, leading to a medium solution between the source and target domains, which is not optimal for the unbalanced domain adaptation. In this paper, we propose a novel Deep Asymmetric Transfer Network (DATN) to address the problem of unbalanced domain adaptation. Specifically, our model will learn a transfer function from the target domain to the source domain and meanwhile adapting the source domain classifier with more discriminative power to the target domain. By doing this, the deep model is able to adaptively put more emphasis on the resource-rich source domain. To alleviate the scarcity problem of supervised data, we further propose an unsupervised transfer method to propagate the knowledge from a lot of unsupervised data by minimizing the distribution discrepancy over the unlabeled data of two domains. The experiments on two real-world datasets demonstrate that DATN attains a substantial gain over state-of-the-art methods

Experimental study on the interrelation of multiple mechanical parameters in overburden rock caving process during coal mining in longwall panel

Abstract In order to comprehend the dynamic disaster mechanism induced by overburden rock caving during the advancement of a coal mining face, a physical simulation model is constructed basing on the geological condition of the 21221 mining face at Qianqiu coal mine in Henan Province, China. This study established, a comprehensive monitoring system to investigate the interrelations and evolutionary characteristics among multiple mechanical parameters, including mining-induced stress, displacement, temperature, and acoustic emission events during overburden rock caving. It is suggested that, despite the uniformity of the overburden rock caving interval, the main characteristic of overburden rock lies in its uneven caving strength. The mining-induced stress exhibits a reasonable interrelation with the displacement, temperature, and acoustic emission events of the rock strata. With the advancement of the coal seam, the mining-induced stress undergoes four successive stages: gentle stability, gradual accumulation, high-level mutation, and a return to stability. The variations in other mechanical parameters does not synchronize with the significant changes in mining-induced stress. Before the collapse of overburden rock occurs, rock strata temperature increment decreases and the acoustic emission ringing counts surges with the increase of rock strata displacement and mining-induced stress. Therefore, the collaborative characteristics of mining-induced stress, displacement, temperature, and acoustic emission ringing counts can be identified as the precursor information or overburden rock caving. These results are in good consistent with on-site situation in the coal mine

A novel multiplex assay of SNP-STR markers for forensic purpose.

Like DIP-STR markers (deletion/insertion polymorphism-short tandem repeat combinations), SNP-STR markers (single nucleotide polymorphism-STR combinations) are also valuable in forensic DNA mixture analysis. In this study, eight SNP-STRs were selected, and a stable and sensitive multiplex polymerase chain reaction (PCR) assay was developed for amplifying these SNP-STRs and the Amelogenin gender marker according to the principle of amplification refractory mutation system (ARMS). This novel multiplex set allows detection of the minor DNA contributor in a DNA mixture of any gender and cellular origin with high resolution (beyond a DNA ratio of 1:20). In addition, SNP-STR haplotype frequencies were estimated based on a survey of 350 unrelated individuals from Chinese Han population, and the combined power of discrimination (PD) and power of exclusion (PE) of the eight SNP-STRs were calculated as 0.99999999965 and 0.9996, which were obviously higher than that of the eight STR loci: 0.9999999954 and 0.9989 respectively. The results indicated that the SNP-STR compound markers have higher application value in forensic identification compared to standard autosomal STRs, especially in the analysis of imbalanced DNA mixtures

Mutation analysis of 21 autosomal short tandem repeats in Han population from Hunan, China

Background: Short tandem repeats (STRs) are powerful genetic markers widely used in human genetics. Population data and locus-specific mutation rates of STRs are crucial for the evaluation and interpretation of genetic evidence in forensic and population genetics. Aim: To investigate the mutation rates of 21 autosomal STRs in a population from central south China. Subjects and methods: This study analysed 3420 paternity cases with a Combined Paternity Index >10,000 from Han population in Hunan. A total of 68,743 meiotic transfers were analysed and 62 mutations were identified. Results: The overall mutation rate of STR loci was 0.9 × 10−3 (95% CI, 0.0007–0.0011) and the locus-specific mutation rates were estimated ranging from 0.0000–0.0023. Locus D1S1656 exhibited the highest mutation rate of 2.3 × 10−3 (95% CI, 0.0005–0.0006), followed by D12S391 with a mutation rate of 2.0 × 10−3 (95% CI, 0.0007–0.0044). No mutation was observed at TPOX, D2S1338 or Penta D. One-step mutation cases accounted for 96.77% of total mutations and the ratio of paternal vs maternal mutations was ∼4.85:1. Inter-population comparisons of locus-specific mutation rates of several STRs revealed significant differences between Han in Hunan and Han in other regions of China. Conclusion: The data justified the use of geographical data in further genetic applications

Influence of fault slip on mining-induced pressure and optimization of roadway support design in fault-influenced zone

This paper presents an investigation on the characteristics of overlying strata collapse and mining-induced pressure in fault-influenced zone by employing the physical modeling in consideration of fault structure. The precursory information of fault slip during the underground mining activities is studied as well. Based on the physical modeling, the optimization of roadway support design and the field verification in fault-influenced zone are conducted. Physical modeling results show that, due to the combined effect of mining activities and fault slip, the mining-induced pressure and the extent of damaged rock masses in the fault-influenced zone are greater than those in the uninfluenced zone. The sharp increase and the succeeding stabilization of stress or steady increase in displacement can be identified as the precursory information of fault slip. Considering the larger mining-induced pressure in the fault-influenced zone, the new support design utilizing cables is proposed. The optimization of roadway support design suggests that the cables can be anchored in the stable surrounding rocks and can effectively mobilize the load bearing capacity of the stable surrounding rocks. The field observation indicates that the roadway is in good condition with the optimized roadway support design