GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?

Large-scale graphs with node attributes are fundamental in real-world scenarios, such as social and financial networks. The generation of synthetic graphs that emulate real-world ones is pivotal in graph machine learning, aiding network evolution understanding and data utility preservation when original data cannot be shared. Traditional models for graph generation suffer from limited model capacity. Recent developments in diffusion models have shown promise in merely graph structure generation or the generation of small molecular graphs with attributes. However, their applicability to large attributed graphs remains unaddressed due to challenges in capturing intricate patterns and scalability. This paper introduces GraphMaker, a novel diffusion model tailored for generating large attributed graphs. We study the diffusion models that either couple or decouple graph structure and node attribute generation to address their complex correlation. We also employ node-level conditioning and adopt a minibatch strategy for scalability. We further propose a new evaluation pipeline using models trained on generated synthetic graphs and tested on original graphs to evaluate the quality of synthetic data. Empirical evaluations on real-world datasets showcase GraphMaker's superiority in generating realistic and diverse large-attributed graphs beneficial for downstream tasks.Comment: Code available at https://github.com/Graph-COM/GraphMake

KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.Comment: 13 pages, 7 figures, 11 tables. Under Revie

Potential association of pulmonary tuberculosis with genetic polymorphisms of toll-like receptor 9 and interferon-gamma in a Chinese population

Abstract Background Association studies have been employed to investigate the relationships between host single nucleotide polymorphisms (SNPs) and susceptibility to pulmonary Tuberculosis (PTB). However, such candidate genetic markers have not been widely studied in Chinese population, especially with respect to the disease development from latent M. tuberculosis infection (LTBI). Methods In this case–control study, 44 candidate SNPs were examined in a total of 600 participants (PTB patients, LTBI controls and healthy controls without M. tuberculosis infection) from Zhengzhou, China. The two groups of controls were frequency matched on gender and age with PTB patients. Genotyping was carried out by the Illumina Golden Gate assay. Results When comparing PTB patients with LTBI controls but not healthy controls without M. tuberculosis infection, significant associations with disease development were observed for TLR9 1174 A/G, TLR9 1635 A/G and IFNG 2109G/A. The two loci in TLR9 were in LD in our study population (r2=0.96, D’=1.00). A combined effect of the genotypes associated with increased risk of PTB (i.e. TLR9 1174G/G and IFNG 2109 A/A) was found when comparing PTB patients with LTBI controls (p=0.004) but not with healthy controls without infection (p=0.433). Conclusions Potential associations between TLR9 and IFN-γ genetic polymorphisms and PTB were observed in a Chinese population which supports further study of the roles played by TLR9/IFN-γ pathway during the development of PTB. </jats:sec

Numerical Simulation of Crack Propagation of Architecture Glass Under Explosion and Impact Load

Abstract Crack initiation and propagation is a long-standing difficulty in solid mechanics, especially for elastic-brittle material. To explore the damage and crack propagation behavior of architectural glass under different type of loads, the element deletion (ED), discontinuous Galerkin peridynamics (DG-PD) and meshless peridynamics (M-PD) methods are studied. Taking the architecture glass as an example, the crack propagation behavior under the bullet impact and explosion load are studied. The JH-2 material model is used in the ED method, and the maximum principal stress and maximum principal strain failure criteria are applied at the same time. In the DG-PD method, it conducts a node separation operation and imposes the criterion of the critical energy release rate. The M-PD method adopts a self-programmed particle discretization method and imposes a criterion of critical elongation. Three methods can simulate the crack growth behavior of glass material, but the PD method has great advantages in detail, such as crack bifurcation and penetration. For low-velocity bullets, the failure behavior of glass all shows cross-shaped cracks in different methods. The splashing of elements or particles appears in the two PD methods, but the particle splashing of the M-PD method is more obvious, and the DG-PD method captures the crack bifurcation effect better. For the failure behavior of glass under explosive loading, the PD method is obviously better than the ED method in terms of modal appearance. However, in the mechanical behavior of specific elements, the two methods have a high degree of agreement.</jats:p

Experiment and Numerical Simulation of Damage Progression in Transparent Sandwich Structure under Impact Load

Crack initiation and propagation is a long-standing difficulty in solid mechanics, especially for elastic brittle materials. A new type of transparent sandwich structure, with a magnesium–aluminum spinel ceramic glass as the outer structure, was proposed in this paper. Its dynamic response was studied by high-speed impact experiments and numerical simulations of peridynamics under impact loads, simultaneously. In the experiments, a light gas cannon was used to load the projectile to 180 m/s, and the front impacted the transparent sandwich structure. In the numerical simulations, the discontinuous Galerkin peridynamics method was adopted to investigate the dynamic response of the transparent sandwich structure. We found that both the impact experiments and the numerical simulations could reproduce the crack propagation process of the transparent sandwich structure. The radial cracks and circumferential cracks of the ceramic glass layer and the inorganic glass layer were easy to capture. Compared with the experiments, the numerical simulations could easily observe the damage failure of every layer and the splashing of specific fragments of the transparent sandwich structure. The ceramic glass layer and the inorganic glass layer absorbed the most energy in the impact process, which is an important manifestation of the impact resistance of the transparent sandwich structure.</jats:p

<i>De novo</i> generation of dual-target ligands using adversarial training and reinforcement learning

Abstract Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an extremely difficult challenge. In this work, we conceive a novel computational framework, herein called dual-target ligand generative network (DLGN), for the de novo generation of bioactive molecules toward two given objectives. Via adversarial training and reinforcement learning, DLGN treats a sequence-based simplified molecular input line entry system (SMILES) generator as a stochastic policy for exploring chemical spaces. Two discriminators are then used to encourage the generation of molecules that belong to the intersection of two bioactive-compound distributions. In a case study, we employ our methods to design a library of dual-target ligands targeting dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. Experimental results demonstrate that the proposed model can generate novel compounds with high similarity to both bioactive datasets in several structure-based metrics. Our model exhibits a performance comparable to that of various state-of-the-art multi-objective molecule generation models. We envision that this framework will become a generally applicable approach for designing dual-target drugs in silico.</jats:p

Experiment and Numerical Simulation of Damage Progression in Transparent Sandwich Structure under Impact Load

Crack initiation and propagation is a long-standing difficulty in solid mechanics, especially for elastic brittle materials. A new type of transparent sandwich structure, with a magnesium&ndash;aluminum spinel ceramic glass as the outer structure, was proposed in this paper. Its dynamic response was studied by high-speed impact experiments and numerical simulations of peridynamics under impact loads, simultaneously. In the experiments, a light gas cannon was used to load the projectile to 180 m/s, and the front impacted the transparent sandwich structure. In the numerical simulations, the discontinuous Galerkin peridynamics method was adopted to investigate the dynamic response of the transparent sandwich structure. We found that both the impact experiments and the numerical simulations could reproduce the crack propagation process of the transparent sandwich structure. The radial cracks and circumferential cracks of the ceramic glass layer and the inorganic glass layer were easy to capture. Compared with the experiments, the numerical simulations could easily observe the damage failure of every layer and the splashing of specific fragments of the transparent sandwich structure. The ceramic glass layer and the inorganic glass layer absorbed the most energy in the impact process, which is an important manifestation of the impact resistance of the transparent sandwich structure