DropMessage: Unifying Random Dropping for Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate noises into models by randomly masking parts of the input. However, some open-ended problems of random dropping on GNNs remain to solve. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, random noises introduced to GNNs cause the incomplete coverage of parameters and unstable training process. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the message matrix and can be applied to any message-passing GNNs. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, which makes it a theoretical upper bound of other methods. Also, we unify existing random dropping methods into our framework and analyze their effects on GNNs. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has both advantages of effectiveness and generalization

SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a fine-grained message propagation module -- upon a novel neighbor-aware self-training mechanism -- in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://github.com/CrownX/SPA.Comment: NeurIPS 2023 camera read

Holistic analysis of lysine acetylation in aquaculture pathogenic bacteria Vibrio alginolyticus under bile salt stress

Lysine acetylation modification is a dynamic and reversible post-translational modification, which plays an important role in the metabolism and pathogenicity of pathogenic bacteria. Vibrio alginolyticus is a common pathogenic bacterium in aquaculture, and bile salt can trigger the expression of bacterial virulence. However, little is known about the function of lysine acetylation in V. alginolyticus under bile salt stress. In this study, 1,315 acetylated peptides on 689 proteins were identified in V. alginolyticus under bile salt stress by acetyl-lysine antibody enrichment and high-resolution mass spectrometry. Bioinformatics analysis found that the peptides motif ****A*Kac**** and *******Kac****A* were highly conserved, and protein lysine acetylation was involved in regulating various cellular biological processes and maintaining the normal life activities of bacteria, such as ribosome, aminoacyl-tRNA biosynthesis, fatty acid metabolism, two-component system, and bacterial secretion system. Further, 22 acetylated proteins were also found to be related to the virulence of V. alginolyticus under bile salt stress through secretion system, chemotaxis and motility, and adherence. Finally, comparing un-treated and treated with bile salt stress lysine acetylated proteins, it was found that there were 240 overlapping proteins, and found amino sugar and nucleotide sugar metabolism, beta-Lactam resistance, fatty acid degradation, carbon metabolism, and microbial metabolism in diverse environments pathways were significantly enriched in bile salt stress alone. In conclusion, this study is a holistic analysis of lysine acetylation in V. alginolyticus under bile salt stress, especially many virulence factors have also acetylated

Understanding Negative Sampling in Graph Representation Learning

Graph representation learning has been extensively studied in recent years. Despite its potential in generating continuous embeddings for various networks, both the effectiveness and efficiency to infer high-quality representations toward large corpus of nodes are still challenging. Sampling is a critical point to achieve the performance goals. Prior arts usually focus on sampling positive node pairs, while the strategy for negative sampling is left insufficiently explored. To bridge the gap, we systematically analyze the role of negative sampling from the perspectives of both objective and risk, theoretically demonstrating that negative sampling is as important as positive sampling in determining the optimization objective and the resulted variance. To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution. With the guidance of the theory, we propose MCNS, approximating the positive distribution with self-contrast approximation and accelerating negative sampling by Metropolis-Hastings. We evaluate our method on 5 datasets that cover extensive downstream graph learning tasks, including link prediction, node classification and personalized recommendation, on a total of 19 experimental settings. These relatively comprehensive experimental results demonstrate its robustness and superiorities.Comment: KDD 202

Comparison of the effects of rumen-protected and unprotected L-leucine on fermentation parameters, bacterial composition, and amino acids metabolism in in vitro rumen batch cultures

This study was conducted to compare the effects of rumen-protected (RP-Leu) and unprotected L-leucine (RU-Leu) on the fermentation parameters, bacterial composition, and amino acid metabolism in vitro rumen batch incubation. The 5.00 g RP-Leu or RU-Leu products were incubated in situ in the rumen of four beef cattle (Bos taurus) and removed after 0, 2, 4, 6, 12, 16, and 24 h to determine the rumen protection rate. In in vitro incubation, both RP-Leu and RU-Leu were supplemented 1.5 mmol/bottle (L-leucine HCl), and incubated after 0, 2, 4, 6, 8, 12, and 16 h to measure gas production (GP), nutrient degradability, fermentation parameters, bacterial composition, and amino acids metabolism. Results from both in vitro and in situ experiments confirmed that the rumen protection rate was greater (p < 0.01) in RP-Leu than in RU-Leu, whereas the latter was slow (p < 0.05) degraded within incubation 8 h. Free leucine from RP-Leu and RU-Leu reached a peak at incubation 6 h (p < 0.01). RU-Leu supplementation increased (p < 0.05) gas production, microbial crude protein, branched-chain AAs, propionate and branched-chain VFAs concentrations, and Shannon and Sobs index in comparison to the control and RP-Leu supplementation. RU-Leu and RP-Leu supplementation decreased (p < 0.05) the relative abundance of Bacteroidota, which Firmicutes increased (p < 0.05). Correlation analysis indicated that there are 5 bacteria at the genus level that may be positively correlated with MCP and propionate (p < 0.05). Based on the result, we found that RP-Leu was more stable than RU-Leu in rumen fluid, but RU-Leu also does not exhibit rapid degradation by ruminal microbes for a short time. The RU-Leu was more beneficial in terms of regulating rumen fermentation pattern, microbial crude protein synthesis, and branched-chain VFAs production than RP-Leu in vitro rumen conditions

Evidence of a resonant structure in the e+e−→π+D0D∗−e^+e^-\to \pi^+D^0D^{*-} cross section between 4.05 and 4.60 GeV

The cross section of the process $e^+e^-\to \pi^+D^0D^{*-}$ for center-of-mass energies from 4.05 to 4.60~GeV is measured precisely using data samples collected with the BESIII detector operating at the BEPCII storage ring. Two enhancements are clearly visible in the cross section around 4.23 and 4.40~GeV. Using several models to describe the dressed cross section yields stable parameters for the first enhancement, which has a mass of 4228.6 \pm 4.1 \pm 6.3 \un{MeV}/c^2 and a width of 77.0 \pm 6.8 \pm 6.3 \un{MeV}, where the first uncertainties are statistical and the second ones are systematic. Our resonant mass is consistent with previous observations of the $Y(4220)$ state and the theoretical prediction of a $D\bar{D}_1(2420)$ molecule. This result is the first observation of $Y(4220)$ associated with an open-charm final state. Fits with three resonance functions with additional $Y(4260)$, $Y(4320)$, $Y(4360)$, $\psi(4415)$, or a new resonance, do not show significant contributions from either of these resonances. The second enhancement is not from a single known resonance. It could contain contributions from $\psi(4415)$ and other resonances, and a detailed amplitude analysis is required to better understand this enhancement