HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that simple homogeneous graph model without meta-path can also achieve comparable results, which calls into question the necessity of meta-path. In this paper, we first present the intrinsic difference about meta-path-based and meta-path-free models, i.e., how to select neighbors for node aggregation. Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs). The core of HAGNN is to leverage the meta-path neighbors and the directly connected neighbors simultaneously for node aggregations. HAGNN divides the overall aggregation process into two phases: meta-path-based intra-type aggregation and meta-path-free inter-type aggregation. During the intra-type aggregation phase, we propose a new data structure called fused meta-path graph and perform structural semantic aware aggregation on it. Finally, we combine the embeddings generated by each phase. Compared with existing heterogeneous GNN models, HAGNN can take full advantage of the heterogeneity in heterogeneous graphs. Extensive experimental results on node classification, node clustering, and link prediction tasks show that HAGNN outperforms the existing modes, demonstrating the effectiveness of HAGNN

Simple and Efficient Partial Graph Adversarial Attack: A New Perspective

As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack methods treat all nodes in the graph as their attack targets. Although existing methods have achieved excellent results, there is still considerable space for improvement. The key problem is that the current approaches rigidly follow the definition of global attacks. They ignore an important issue, i.e., different nodes have different robustness and are not equally resilient to attacks. From a global attacker's view, we should arrange the attack budget wisely, rather than wasting them on highly robust nodes. To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets. First, to select the vulnerable items, we propose a hierarchical target selection policy, which allows attackers to only focus on easy-to-attack nodes. Then, we propose a cost-effective anchor-picking policy to pick the most promising anchors for adding or removing edges, and a more aggressive iterative greedy-based attack method to perform more efficient attacks. Extensive experimental results demonstrate that PGA can achieve significant improvements in both attack effect and attack efficiency compared to other existing graph global attack methods

DIFER: Differentiable Automated Feature Engineering

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to treating AutoFE as a coarse-grained black-box optimization problem over a discrete space. In this work, we propose an efficient gradient-based method called DIFER to perform differentiable automated feature engineering in a continuous vector space. DIFER selects potential features based on evolutionary algorithm and leverages an encoder-predictor-decoder controller to optimize existing features. We map features into the continuous vector space via the encoder, optimize the embedding along the gradient direction induced by the predicted score, and recover better features from the optimized embedding by the decoder. Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.Comment: 8 pages, 5 figure

Zeolite-cage-lock strategy for in situ synthesis of highly nitrogen-doped porous carbon for selective separation of carbon dioxide gas

Porous carbon structures doped with 18.14% nitrogen and prepared by a carbonizing organic template in ZSM-39 zeolitic cages show high CO2 adsorption capacity.</p

Polar phase transitions in heteroepitaxial stabilized La0.5Y0.5AlO3 thin films

PAPER Polar phase transitions in heteroepitaxial stabilized La0.5Y0.5AlO3 thin films Shenghua Liu1, Chunfeng Zhang1, Mengya Zhu1, Qian He2, Jak Chakhalian3, Xiaoran Liu3,4, Albina Borisevich2, Xiaoyong Wang1 and Min Xiao1,4 Published 1 September 2017 • © 2017 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 29, Number 40 Article PDF Figures References PDF 18 Total downloads Turn on MathJax Get permission to re-use this article Share this article Article information Abstract We report on the fabrication of epitaxial La0.5Y0.5AlO3 ultrathin films on (001) LaAlO3 substrates. Structural characterizations by scanning transmission electron microscopy and x-ray diffraction confirm the high quality of the film with a − b + c − AlO6 octahedral tilt pattern. Unlike either of the nonpolar parent compound, LaAlO3 and YAlO3, second harmonic generation measurements on the thin films suggest a nonpolar–polar phase transition at T c near 500 K, and a polar–polar phase transition at T a near 160 K. By fitting the angular dependence of the second harmonic intensities, we further propose that the two polar structures can be assigned to the Pmc2 1 and Pmn2 1 space group, while the high temperature nonpolar structure belongs to the Pbnm space group

Unique allosteric effect driven rapid adsorption of carbon dioxide on a new ionogel [P4444][2-Op]@MCM-41 with excellent cyclic stability and loading-dependent capacity

Allosteric effect-driven rapid stepwise CO2 adsorption of pyridine-containing anion functionalized ionic liquid [P4444][2-Op] confined into mesoporous silica MCM-41.</p

Efficient Thermal Conductance in Organometallic Perovskite CH3NH3PbI3 Films

Perovskite-based optoelectronic devices have shown great promise for solar conversion and other optoelectronic applications, but their long-term performance instability is regarded as a major obstacle to their widespread deployment. Previous works have shown that the ultralow thermal conductivity and inefficient heat spreading might put an intrinsic limit on the lifetime of perovskite devices. Here, we report the observation of a remarkably efficient thermal conductance, with conductivity of 11.2 +/- 0.8 W m^-1 K^-1 at room temperature, in densely-packed perovskite CH3NH3PbI3 films, via noncontact time-domain thermal reflectance measurements. The temperature-dependent experiments suggest the important roles of organic cations and structural phase transitions, which are further confirmed by temperature-dependent Raman spectra. The thermal conductivity at room temperature observed here is over one order of magnitude larger than that in the early report, suggesting that perovskite device performance will not be limited by thermal stability

Laboratory Study on Properties of Diatomite and Basalt Fiber Compound Modified Asphalt Mastic

In order to improve the performance of asphalt mastic, some researchers have added diatomite or basalt fiber as a modifier to the asphalt mastic, and the results show that some properties of the asphalt mastic were improved. For the simultaneous addition of diatomite and basalt fiber, two kinds of modifier, compound modified asphalt mastic had not been reported; in this paper, thirteen groups of diatomite and basalt fiber (DBFCMAM) compound modified asphalt mastic with different content were prepared to study the performance. Softening point, cone penetration, viscosity, and DSR tests were conducted, for the high temperature performance evaluation of DBFCMAM, whereas force ductility and BBR tests were used in the low temperature performance study of the DBFCMAM. The results demonstrated that the high temperature performance of DBFCMAM was increased; moreover, the low temperature performance of DBFCMAM improved by diatomite and basalt fiber according to the results of the force ductility test; however, the conclusion of the BBR test data was inconsistent with the force ductility test. In summary, the high temperature and low temperature properties of DBFCMAM had been improved