No abstract

Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that pass through them. It is key to many valuable applications, including community detection and network dismantling. Computing BC scores on large networks is computationally challenging due to high time complexity. Many approximation algorithms have been proposed to speed up the estimation of BC, which are mainly sampling-based. However, these methods still prone to considerable execution time on large-scale networks, and their results are often exacerbated when small changes happen to the network structures.In this paper, we focus on identifying nodes with high BC in a graph, since many application scenarios are built upon retrieving nodes with top-k BC. Different from previous heuristic methods, we turn this task into a learning problem and design an encoderdecoder based framework to resolve the problem. More specifically, the encoder leverages the network structure to encode each node into an embedding vector, which captures the important structural information of the node. The decoder transforms the embedding vector for each node into a scalar, which captures the relative rank of this node in terms of BC. We use the pairwise ranking loss to train the model to identify the orders of nodes regarding their BC. By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes. Comprehensive experiments on both synthetic and real-world networks demonstrate that, compared to representative baselines, our model drastically speeds up the prediction without noticeable sacrifice in accuracy, and outperforms the state-of-the-art by accuracy on several large real-world networks.

In recent years, methylated thioarsenicals have been widely detected in various biological and environmental matrices, suggesting their broad involvement and biological importance in arsenic metabolism. However, very little is known about the formation mechanism of methylated thioarsenicals and the relation between arsenic methylation and thiolation processes. It is timely and necessary to summarize and synthesize the reported information on thiolated arsenicals for an improved understanding of arsenic thiolation. To this end, we examined the proposed formation pathways of methylated oxoarsenicals and thioarsenicals from a chemical perspective and proposed a novel arsenic metabolic scheme, in which arsenic thiolation is integrated with methylation (instead of being separated from methylation as currently reported). We suggest in the new scheme that protein-bound pentavalent arsenicals are critical intermediates that connect methylation and thiolation, with protein binding of pentavalent methylated thioarsenical being a key step for arsenic thiolation. This informative review on arsenic thiolation from the chemical perspective will be helpful to better understand the arsenic metabolism at the molecular level and the toxicological effects of arsenic species.