Scalable and Structural Multi-View Graph Clustering With Adaptive Anchor Fusion.

Anchor graph has been recently proposed to accelerate multi-view graph clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs and combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a separable two-step procedure-anchor graph construction and individual graph fusion, which may degrade the clustering performance.

Improved Dual Correlation Reduction Network With Affinity Recovery.

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observe that the existing methods suffer from the representation collapse problem and tend to encode samples with different classes into the same latent embedding. Consequently, the discriminative capability of nodes is limited, resulting in suboptimal clustering performance.

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal.

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According to the graph types, existing KGR models can be roughly divided into three categories, i.

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment.

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood.

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network.

With the development of various applications, such as recommendation systems and social network analysis, graph data have been ubiquitous in the real world. However, graphs usually suffer from being absent during data collection due to copyright restrictions or privacy-protecting policies. The graph absence could be roughly grouped into attribute-incomplete and attribute-missing cases.

Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pretraining.

Contrastive learning has recently emerged as a powerful technique for graph self-supervised pretraining (GSP). By maximizing the mutual information (MI) between a positive sample pair, the network is forced to extract discriminative information from graphs to generate high-quality sample representations. However, we observe that, in the process of MI maximization (Infomax), the existing contrastive GSP algorithms suffer from at least one of the following problems: 1) treat all samples equally during optimization and 2) fall into a single contrasting pattern within the graph.

Simple Contrastive Graph Clustering.

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming graph convolutional operations undermine the efficiency of these methods. To solve this problem, we propose a simple contrastive graph clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.

scDFC: A deep fusion clustering method for single-cell RNA-seq data.

Clustering methods have been widely used in single-cell RNA-seq data for investigating tumor heterogeneity. Since traditional clustering methods fail to capture the high-dimension methods, deep clustering methods have drawn increasing attention these years due to their promising strengths on the task. However, existing methods consider either the attribute information of each cell or the structure information between different cells.

Deep Fusion Clustering Network With Reliable Structure Preservation.

Deep clustering, which can elegantly exploit data representation to seek a partition of the samples, has attracted intensive attention. Recently, combining auto-encoder (AE) with graph neural networks (GNNs) has accomplished excellent performance by introducing structural information implied among data in clustering tasks. However, we observe that there are some limitations of most existing works: 1) in practical graph datasets, there exist some noisy or inaccurate connections among nodes, which would confuse network learning and cause biased representations, thus leading to unsatisfied clustering performance; 2) lacking dynamic information fusion module to carefully combine and refine the node attributes and the graph structural information to learn more consistent representations; and 3) failing to exploit the two separated views' information for generating a more robust target distribution.

Context-Integrated and Feature-Refined Network for Lightweight Object Parsing.

Semantic segmentation for lightweight object parsing is a very challenging task, because both accuracy and efficiency (e.g., execution speed, memory footprint or computational complexity) should all be taken into account.

High-performance supercapacitors based on hierarchically porous carbons with a three-dimensional conductive network structure.

Clews of polymer nanobelts (CsPNBs) have the advantages of inexpensive raw materials, simple synthesis and large output. Novel clews of carbon nanobelts (CsCNBs) have been successfully prepared by carbonizing CsPNBs and by KOH activation subsequently. From the optimized process, CsCNBs*4, with a specific surface area of 2291 m2 g-1 and a pore volume of up to 1.

A Universal Strategy To Prepare Sulfur-Containing Polymer Composites with Desired Morphologies for Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries are probably the most promising candidates for the next-generation batteries owing to their high energy density. However, Li-S batteries face severe technical problems where the dissolution of intermediate polysulfides is the biggest problem because it leads to the degradation of the cathode and the lithium anode, and finally the fast capacity decay. Compared with the composites of elemental sulfur and other matrices, sulfur-containing polymers (SCPs) have strong chemical bonds to sulfur and therefore show low dissolution of polysulfides.

Facile Synthesis of Nitrogen and Oxygen Co-Doped Clews of Carbon Nanobelts for Supercapacitors with Excellent Rate Performance.

Facile synthesis of carbon materials with high heteroatom content, large specific surface area (SSA) and hierarchical porous structure is critical for energy storage applications. In this study, nitrogen and oxygen co-doped clews of carbon nanobelts (NCNBs) with hierarchical porous structures are successfully prepared by a carbonization and subsequent activation by using ladder polymer of hydroquinone and formaldehyde (LPHF) as the precursor and ammonia as the activating agent. The hierarchical porous structures and ultra-high SSA (up to 2994 m² g) can effectively facilitate the exchange and transportation of electrons and ions.

Facile Synthesis of Ultrahigh-Surface-Area Hollow Carbon Nanospheres and their Application in Lithium-Sulfur Batteries.

Hollow carbon nanospheres (HCNs) with specific surface areas up to 2949 m  g and pore volume up to 2.9 cm  g were successfully synthesized from polyaniline-co-polypyrrole hollow nanospheres by carbonization and CO activation. The cavity diameter and wall thickness of HCNs can be easily controlled by activation time.