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Graph neural networks (GNNs) are powerful tools for data mining on graph-structured data in various domains, such as social science, finance, and biology. However, most existing GNNs operate in Euclidean space and may fail to preserve the intrinsic network properties, such as self-similarity and hierarchy, that characterize many real-world graphs. Hyperbolic graph neural networks (HGNNs) address this limitation by embedding graphs into hyperbolic space, which can better capture the hierarchical structures of networks. However, HGNNs often involve complex computations in hyperbolic space or its tangent space during training, which may hinder their efficiency. In this paper, we propose hyperbolic attention graph neural networks (HA-GNN), which can leverage both network structure and node features for graph representation learning in an efficient way. Specifically, we design a structural properties attention mechanism that measures the structural connection between nodes based on their hyperbolic embeddings. We also design a node features attention mechanism that quantifies the feature similarity between nodes. We then combine these two attentions to obtain a hyperbolic attention that weights the relevance between all connected nodes. We conduct extensive experiments on five real-world networks and demonstrate that our model consistently and significantly outperforms other state-of-the-art methods. For example, on the Cora network, our model achieves an accuracy of 83.1 (± 0.4) on node classification tasks, which is 1.6% higher than the best baseline method in Euclidean space. [ABSTRACT FROM AUTHOR]

Node ranking is an essential problem in complex networks, which aims to identify influential or central nodes in a graph. Existing methods for node ranking are either computationally expensive or suboptimal for different networks and multiple tasks. In this paper, we propose GNR, a graph neural network-based node ranking model that can sort nodes quickly and efficiently for any type of networks. To achieve this, GNR takes into account three factors to calculate the importance of nodes and train the model: degree, infection score, and dismantling score, which capture the local and global structure of the network and the effect of nodes on network propagation and dismantling. We conduct extensive experiments on 16 real networks, and compare GNR with other centrality-based node ranking models on three tasks: network dismantling, virus propagation, and information spreading. The results show that GNR performs better in most cases and validates the effectiveness and superiority of our model. • Proposing a graph neural network-based node ranking model. • GNR fuses different node importance metrics for generality. • GNR is efficient and scalable for any type of networks. • GNR outperforms other centrality-based methods in various tasks. [ABSTRACT FROM AUTHOR]

Abstract: The photodissociation of n-C7H15Br, near the red wing of the absorption A-band, has been investigated utilizing the velocity mapped ion imaging technique. Both the speed and angular distributions of Br∗(2P1/2) and Br(2P3/2) fragments were determined. The experimental data indicate that the photodissociation in the investigated range originates from adiabatic and non-adiabatic transitions of the three low-lying dissociative electronic states. The fragments kinetic energies and the angular distributions show that about 70% energy is deposited into the internal energy of the fragments. These results can be explained using soft impulsive model. The experiment also shows that the anisotropy parameter for Br∗ is sensitive to the photolysis wavelength. [Copyright &y& Elsevier]

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease. This study aimed to investigate the role of SMAD specific E3 ubiquitin protein ligase 1 (SMURF1) in the Th17 and Th17.1 differentiation and Treg/Th17 imbalance, which are major factors contributing to the pathogenesis of SLE. SLE patients and healthy individuals were recruited to detect the SMURF1 levels in naïve CD4+ cells from peripheral blood. Purified and expanded naïve CD4+ T cells were employed to evaluate the effects of SMURF1 on Th17 and Th17.1 polarization in vitro. MRL/lpr lupus model was employed to explore the disease phenotype as well as Treg/Th17 balance in vivo. The results showed that SMURF1 was down-regulated in naïve CD4+ T cells in peripheral blood of patients with SLE and in spleen of MRL/lpr mice. SMURF1 overexpression suppressed the polarization of naïve CD4+ T cells toward Th17 and Th17.1 phenotype and down-regulated the expression of retinoid-related orphan receptor-gammat (RORγt). Subsequently, SMURF1 down-regulation aggravated the disease phenotype, inflammation, and the Treg/Th17 imbalance in MRL/lpr mice. Furthermore, we found that SMURF overexpression promoted the ubiquitination and decreases the stability of RORγt. In conclusion, SMURF1 inhibited the polarization of Th17 and Th17.1 cells and improved the Treg/Th17 imbalance in SLE, which was mediated as least partly by the ubiquitination of RORγt. • SMURF1 is downregulated in Th0 cells in peripheral blood of patients with SLE. • SMURF1 represses the polarization of Th0 cells toward Th17 and Th17.1 phenotypes. • SMURF1 promotes the ubiquitination and decreases the stability of RORγt. [ABSTRACT FROM AUTHOR]