TP-GCL: graph contrastive learning from the tensor perspective.

Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.

Dual-channel deep graph convolutional neural networks.

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance improvement of the models. Graph convolutional neural networks superimpose multi-layer graph convolution operations, which would occur in smoothing phenomena, resulting in performance decreasing as the increasing number of graph convolutional layers. Inspired by the success of residual connections on convolutional neural networks, this paper applies residual connections to dual-channel graph convolutional neural networks, and increases the depth of dual-channel graph convolutional neural networks. Thus, a dual-channel deep graph convolutional neural network (D2GCN) is proposed, which can effectively avoid over-smoothing and improve model performance. D2GCN is verified on CiteSeer, DBLP, and SDBLP datasets, the results show that D2GCN performs better than the comparison algorithms used in node classification tasks.

Multi-channel high-order network representation learning research.

The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.

Boosting Charge Transport and Catalytic Performance in MoS2 by Zn2+ Intercalation Engineering for Lithium-Sulfur Batteries.

Transition metal dichalcogenides (TMDs) have been widely studied as catalysts for lithium-sulfur batteries due to their good catalytic properties. However, their poor electronic conductivity leads to slow sulfur reduction reactions. Herein, a simple Zn2+ intercalation strategy was proposed to promote the phase transition from semiconducting 2H-phase to metallic 1T-phase of MoS2. Furthermore, the Zn2+ between layers can expand the interlayer spacing of MoS2 and serve as a charge transfer bridge to promote longitudinal transport along the c-axis of electrons. DFT calculations further prove that Zn-MoS2 possesses better charge transfer ability and stronger adsorption capacity. At the same time, Zn-MoS2 exhibits excellent redox electrocatalytic performance for the conversion and decomposition of polysulfides. As expected, the lithium-sulfur battery using Zn0.12MoS2-carbon nanofibers (CNFs) as the cathode has high specific capacity (1325 mAh g-1 at 0.1 C), excellent rate performance (698 mAh g-1 at 3 C), and outstanding cycle performance (it remains 604 mAh g-1 after 700 cycles with a decay rate of 0.045% per cycle). This study provides valuable insights for improving electrocatalytic performance of lithium-sulfur batteries.

LGNN: a novel linear graph neural network algorithm.

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of graph data, and the convolution process has a large amount of calculation. It is necessary to use multi-channel or deep neural network structure to achieve the goal of modeling the high-order structural characteristics of the network. Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric normalization and feature normalization to remove deviations in the structure and features. Then, by designing a high-order adjacency matrix propagation mechanism, LGNN enables nodes to iteratively aggregate and learn the feature information of high-order neighbors. After obtaining the node representation of the network structure, LGNN uses a simple linear mapping to maintain computational efficiency and obtain the final node representation. The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds the machine learning performance of the existing algorithms in most graph neural network performance evaluation tasks, especially on sparse networks.

Construction of urchin-like bimetallic phosphides induced by carbon dots for efficient wide pH hydrogen production.

The development of an efficient noble-metal-free and pH-universal electrocatalyst for the hydrogen evolution reaction (HER) would be highly significant for hydrogen (H2) production via electrocatalytic water splitting. However, developing such a catalyst remains a formidable task. Herein, a strategy is proposed for the in situ fabrication of a novel urchin-like NiCoP microsphere catalyst (0.5CDs-NiCoP/NF) on nickel foam (NF) using carbon dots (CDs) as a directing agent. The strong bonding between the CDs and metals provides additional active sites, giving 0.5CDs-NiCoP/NF excellent electrocatalytic hydrogen evolution performance in environments ranging from acidic to basic. Moreover, the unique structure of 0.5CDs-NiCoP/NF endows this catalyst with low Tafel slopes of 73, 146 and 74 mV dec-1 for HER in acidic, neutral and alkaline conditions, respectively. This performance exceeds that of numerous other reported non-precious HER catalysts. In summary, this work offers a novel and efficient strategy for the design and synthesis of low-cost, efficient, and robust transition metal phosphides (TMPs) electrocatalysts.

TAMNR: a network embedding learning algorithm using text attention mechanism.

Because many existing algorithms are mainly trained based on the structural features of the networks, the results are more inclined to the structural commonality of the networks. These algorithms ignore the rich external information and node attributes (such as node text content, community and labels, etc.) that have important implications for network data analysis tasks. Existing network embedding algorithms considering text features usually regard the co-occurrence words in the node's text, or use an induced matrix completion algorithm to factorize the text feature matrix or the network structure feature matrix. Although this kind of algorithm can greatly improve the network embedding performance, they ignore the contribution rate of different co-occurrence words in the node's text. This article proposes a network embedding learning algorithm combining network structure and co-occurrence word features, also incorporating an attention mechanism to model the weight information of the co-occurrence words in the model. This mechanism filters out unimportant words and focuses on important words for learning and training tasks, fully considering the impact of the different co-occurrence words to the model. The proposed network representation algorithm is tested on three open datasets, and the experimental results demonstrate its strong advantages in node classification, visualization analysis, and case analysis tasks.

Facile Microwave Synthesis of a Narrow-Band Green-Emitting Phosphor Cs3MnBr5 and the Effect of Anion Substitution on Its Luminescence Properties.

A bright blue light excitable and narrow-band green-emitting phosphor Cs3MnBr5 has been synthesized by a facile microwave radiation method within 2 min. The influence of the matrix on its steady-state and transient-state luminescence properties is investigated by partial substitution of Br- ions by Cl- ions. The incorporation of Cl- ions in Cs3Mn(Br1-xClx)5 resulted in almost no change in the emission maxima of Mn2+, which is attributed to the synergistic effect of reduced covalency and increased crystal field strength caused by the replacement of Br- ions by Cl- ions. Meanwhile, the emission of Mn2+ decreases with the increasing Cl- content, which is caused by different thermal quenching of Mn2+ emission in the mixed Cl-/Br- coordination. Moreover, the incorporation of Cl- in Cs3Mn(Br1-xClx)5 was found to have different effects on the lifetime of Mn2+ at different temperatures, that is, at room temperature, the lifetime of Mn2+ decreases with the increasing Cl- content, while at liquid nitrogen temperature, the lifetime of Mn2+ increases upon increasing the Cl- content. The former is due to the different thermal quenching for different coordinations of Mn2+ with Cl- and Br-, while the latter is due to the weaker spin-orbit coupling of the Mn2+ ion caused by the interaction with the lighter Cl- ions, which makes the spin selection rule stricter and leads to a longer lifetime of Mn2+ consequently.

Eigenvalue-based entropy in directed complex networks.

Entropy is an important index for describing the structure, function, and evolution of network. The existing research on entropy is primarily applied to undirected networks. Compared with an undirected network, a directed network involves a special asymmetric transfer. The research on the entropy of directed networks is very significant to effectively quantify the structural information of the whole network. Typical complex network models include nearest-neighbour coupling network, small-world network, scale-free network, and random network. These network models are abstracted as undirected graphs without considering the direction of node connection. For complex networks, modeling through the direction of network nodes is extremely challenging. In this paper, based on these typical models of complex network, a directed network model considering node connection in-direction is proposed, and the eigenvalue entropies of three matrices in the directed network is defined and studied, where the three matrices are adjacency matrix, in-degree Laplacian matrix and in-degree signless Laplacian matrix. The eigenvalue-based entropies of three matrices are calculated in directed nearest-neighbor coupling, directed small world, directed scale-free and directed random networks. Through the simulation experiment on the real directed network, the result shows that the eigenvalue entropy of the real directed network is between the eigenvalue entropy of directed scale-free network and directed small-world network.

Controllability of deterministic networks with the identical degree sequence.

Controlling complex network is an essential problem in network science and engineering. Recent advances indicate that the controllability of complex network is dependent on the network's topology. Liu and Barabási, et.al speculated that the degree distribution was one of the most important factors affecting controllability for arbitrary complex directed network with random link weights. In this paper, we analysed the effect of degree distribution to the controllability for the deterministic networks with unweighted and undirected. We introduce a class of deterministic networks with identical degree sequence, called (x,y)-flower. We analysed controllability of the two deterministic networks ((1, 3)-flower and (2, 2)-flower) by exact controllability theory in detail and give accurate results of the minimum number of driver nodes for the two networks. In simulation, we compare the controllability of (x,y)-flower networks. Our results show that the family of (x,y)-flower networks have the same degree sequence, but their controllability is totally different. So the degree distribution itself is not sufficient to characterize the controllability of deterministic networks with unweighted and undirected.

On the maximum Estrada index of 3-uniform linear hypertrees.

For a simple hypergraph H on n vertices, its Estrada index is defined as [Formula in text], where λ 1, λ 2,…, λ n are the eigenvalues of its adjacency matrix. In this paper, we determine the unique 3-uniform linear hypertree with the maximum Estrada index.

Facile synthesis of Mn-doped ZnS nanocrystals and determination of critical temperature for lattice diffusion process.

High-quality Mn:ZnS doped nanocrystals (d-dots) with photoluminescence (PL) quantum yield (QY) of 50-70% have been synthesized based on nucleation-doping strategy by choosing 1-dodecanethiol (DDT) as the capping ligand. Controlling the growth of small-sized MnS core nanoclusters was successfully achieved by changing the injection temperature of sulfur precursor, the growth time of MnS nuclei, and the amount of DDT. Furthermore, MnS/ZnS core/shell d-dots with a diffusion layer at the interface between the MnS core and the ZnS shell were fabricated through an overcoating of the ZnS shell layer on the presynthesized MnS core nanoclusters. The resulting monodisperse d-dots exhibited spherical shape with a zinc-blende crystal structure. The critical temperature for lattice diffusion of Mn ions in the ZnS host lattice was determined to be about 260 degrees C by annealing the presynthesized and purified Mn:ZnS d-dots.