Graph Condensation via Eigenbasis Matching

The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications. Recently, the emerging graph condensation (GC) sheds light on reducing the computational cost of GNNs from a data perspective. It aims to replace the real large graph with a significantly smaller synthetic graph so that GNNs trained on both graphs exhibit comparable performance. However, our empirical investigation reveals that existing GC methods suffer from poor generalization, i.e., different GNNs trained on the same synthetic graph have obvious performance gaps. What factors hinder the generalization of GC and how can we mitigate it? To answer this question, we commence with a detailed analysis and observe that GNNs will inject spectrum bias into the synthetic graph, resulting in a distribution shift. To tackle this issue, we propose eigenbasis matching for spectrum-free graph condensation, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs. Subsequently, GCEM leverages the spectrum of the real graph and the synthetic eigenbasis to construct the synthetic graph, thereby preserving the essential structural information. We theoretically demonstrate that the synthetic graph generated by GCEM maintains the spectral similarity, i.e., total variation, of the real graph. Extensive experiments conducted on five graph datasets verify that GCEM not only achieves state-of-the-art performance over baselines but also significantly narrows the performance gaps between different GNNs.Comment: Under Revie

Structural Deep Clustering Network

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.Comment: Published at The Web Conference (WWW) 2020, full pape

Beyond Low-frequency Information in Graph Convolutional Networks

Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which gives rise to one fundamental question: is the low-frequency information all we need in the real world applications? In this paper, we first present an experimental investigation assessing the roles of low-frequency and high-frequency signals, where the results clearly show that exploring low-frequency signal only is distant from learning an effective node representation in different scenarios. How can we adaptively learn more information beyond low-frequency information in GNNs? A well-informed answer can help GNNs enhance the adaptability. We tackle this challenge and propose a novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a self-gating mechanism, which can adaptively integrate different signals in the process of message passing. For a deeper understanding, we theoretically analyze the roles of low-frequency signals and high-frequency signals on learning node representations, which further explains why FAGCN can perform well on different types of networks. Extensive experiments on six real-world networks validate that FAGCN not only alleviates the over-smoothing problem, but also has advantages over the state-of-the-arts

Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea

Dietary inflammation index is associated with dyslipidemia: evidence from national health and nutrition examination survey, 1999–2019

Abstract Background and aims This study aimed to investigate the association between the Dietary Inflammatory Index (DII) and dyslipidemia, as well as to evaluate the mortality risk associated with DII in participants with dyslipidemia. Methods Data from the National Health and Nutrition Examination Survey database were divided into dyslipidemia and non-dyslipidemia groups. The association between DII and dyslipidemia was investigated using the weighted chi-square test, weighted t-test, and weighted logistic regression. Weighted Cox proportional hazards models were used to estimate the hazard ratios and 95% confidence intervals for all-cause and cardiovascular disease-related mortality within the dyslipidemia group. Results A total of 17,820 participants, including 4,839 without and 12,981 with dyslipidemia were analyzed in this study. The results showed that DII was higher in the dyslipidemia group compared to the non-dyslipidemia group (1.42 ± 0.03 vs. 1.23 ± 0.04, P < 0.01). However, for energy, protein, carbohydrates, total fat, saturated fat, and iron, DII was lower in participants with dyslipidemia. Logistic regression analysis revealed a strong positive association between DII and dyslipidemia. The odds ratios for dyslipidemia from Q1 to Q4 were 1.00 (reference), 1.12 (0.96–1.31), 1.23 (1.04–1.44), and 1.33 (1.11–1.59), respectively. In participants with dyslipidemia, a high DII was associated with high all-cause and cardiovascular mortality. Conclusion DII was closely associated with dyslipidemia. A pro-inflammatory diet may play a role in unfavorable consequences and is linked to both all-cause mortality and cardiovascular death in patients with dyslipidemia. Participants with dyslipidemia should pay attention to their anti-inflammatory dietary patterns

Ab initio thermodynamic optimization of Ni-rich Ni-Co-Mn oxide cathode coatings

The effectiveness of surface coatings in improving the stability and cycling performance of cathodes has been demonstrated since they are first proposed in the 1990's. However, the progress since then is made mostly using the trial-and-error method. Herein; an automated electrochemical-chemical stability design scheme based on first-principles thermodynamics calculations of reaction models is presented to optimize coatings for Ni-rich nickel-cobalt-manganese oxide (NCM) cathodes. Given that the coating must possess a wider electrochemical window than the cathode without the occurrence of Li-ion redistribution at the cathode/coating interface, the reaction energies of both lithium insertion/extraction and decomposition process associated with the coating are used as one of the two screening criteria. As the coating is also required to be chemically stable in Li residues and hydrofluoric-acid containing liquid environment, the positive reaction energy achieved by adjusting molar ratio of the components is used as another criterion. Using these two screening criteria, we demonstrate that lithium-containing metal phosphates, rather than previously suggested Li-containing metal oxides, are the optimal coatings for Ni-rich NCM cathodes, which is confirmed experimentally. The proposed approach is general and can be used to find optimal coating materials for any other cathodes