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Realistically representing the Arctic amplification in global climate models (GCMs) represents a key to accurately predict the climate system's response to increasing anthropogenic forcings. We examined the amplified Arctic warming over the past century simulated by 36 state‐of‐the‐art GCMs against observation. We found a clear difference between the simulations and the observation in terms of the evolution of the secular warming rates. The observed rates of the secular Arctic warming increase from 0.14 °C/10a in the early 1890s to 0.21 °C/10a in the mid‐2010s, while the GCMs show a negligible trend to 0.35 °C/10a at the corresponding times. The overestimation of the secular warming rate in the GCMs starts from the mid‐twentieth century and aggravates with time. Further analysis indicates that the overestimation mainly comes from the exaggerated heating contribution from the Arctic sea ice melting. This result implies that the future secular Arctic warming may have been over‐projected. Key Points: The anthropogenic emissions most likely determined the nonlinear, secular trend of Arctic warming during the past centuryState‐of‐the‐art climate models overestimate the secular Arctic warming rate since the mid‐twentieth centuryAggravating secular warming rate with time may lead to over-projection of the future Arctic warming [ABSTRACT FROM AUTHOR]

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real-world networks and should be considered for community detection, in this article, we propose an algorithm, called link-based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over-overlapping and incorrect partition of weak ties. Experimental results on a large number of real-world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks. [ABSTRACT FROM AUTHOR]

In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description. In this article, we only focus on the node‐attributed graph. According to the properties of structure and attribute in node‐attributed graphs, we define a novel dense subgraph pattern, called hybridized k‐truss in attribute‐augmented graph. A hybridized k‐truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least (k − 2) common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node‐attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over state‐of‐the‐art methods, through comparison about structural cohesiveness and attributive homogeneity. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics‐based Overlapping Semantic Community detection algorithm(DOSC) for node‐attribute networks. The method is divided into three phases: Firstly, we detect local single‐attribute subcommunities in each attribute‐induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real‐world networks demonstrate that DOSC is a more effective semantic community detection method compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]