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Article
Publication date: 4 July 2024

Weijiang Wu, Heping Tan and Yifeng Zheng

Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively…

96

Abstract

Purpose

Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

Design/methodology/approach

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Findings

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

Originality/value

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Available. Open Access. Open Access
Article
Publication date: 22 September 2021

Jin Tang, Weijiang Li, Jiayi Fang, Zhonghao Zhang, Shiqiang Du, Yanjuan Wu and Jiahong Wen

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at…

2105

Abstract

Purpose

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Design/methodology/approach

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Findings

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

Originality/value

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.

Details

International Journal of Climate Change Strategies and Management, vol. 13 no. 4/5
Type: Research Article
ISSN: 1756-8692

Keywords

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Article
Publication date: 5 March 2019

Yang Liu, Ping Deng, Jiang Wei, Ying Ying and Mu Tian

The purpose of this paper is to examine the relationships between environment turbulence, knowledge transfer and innovation performance for emerging market multinationals (EMNEs…

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Abstract

Purpose

The purpose of this paper is to examine the relationships between environment turbulence, knowledge transfer and innovation performance for emerging market multinationals (EMNEs) in an asymmetric international R&D alliance.

Design/methodology/approach

Data were collected through a survey of high-tech firms in Zhejiang Province of China from 2013 to 2015.

Findings

Innovation performance of EMNEs is positively influenced by knowledge transfer activities (knowledge replication and knowledge adaption), technological and market turbulence, while negatively influenced by institutional turbulence. In addition, different aspects of environmental turbulence moderate the relationship between knowledge transfer practices and innovation performance of EMNEs differently.

Research limitations/implications

Future studies could use a longitudinal design to capture the dynamism driving innovation performance of EMNEs through R&D alliances.

Practical implications

Practical guidelines are provided particularly for EMNE managers on how to develop an innovation strategy by leveraging external knowledge, adaptive innovation and environmental turbulence.

Originality/value

This study deepens the knowledge of how EMNEs enhance their innovation by building the linkage between environmental turbulence and absorptive capacity through knowledge transfer activities in an asymmetric international R&D alliance.

Details

Journal of Business & Industrial Marketing, vol. 34 no. 6
Type: Research Article
ISSN: 0885-8624

Keywords

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