scholarly journals Industrial Productivity Divergence and Input-Output Network Structures: Evidence from Japan 1973–2012

Economies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 52 ◽  
Author(s):  
Alvaro Domínguez ◽  
Carlos Mendez
Keyword(s):  
Detection Algorithm ◽  
Network Structures ◽  
Input Output ◽  
Community Membership ◽  
Output Structure ◽  
Sigma Convergence ◽  
Industrial Productivity ◽  
Divergence Pattern ◽  
Community Detection Algorithm ◽  
Shed Light

Since the early 1990s, there have been larger and increasing labor productivity differences across industries in Japan. More specifically, a clear pattern of sigma and beta divergence across industries is observed. To shed light on these stylized facts, we first evaluate the input–output structure of Japan through the lens of a community-detection algorithm from network theory. Results from this analysis suggest the existence of two input–output network structures: a densely-connected group of industries (a stationary community), whose members remain in it throughout the period; and a group of industries (a transitional community) whose members do not belong to this first group. Next, we re-evaluate the industrial divergence pattern of Japan in the context of each network structure. Results suggest that divergence is mostly driven by the transitional community. Interestingly, since 2007, a pattern of sigma convergence started to re-appear only in the stationary community. We conclude suggesting that industrial divergence and instability in community membership are not necessarily indicative of low productivity performance.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals Risk Management in Seaports: A Community Analysis at the Port of Hamburg

2021 ◽  
Vol 13 (14) ◽  
pp. 8035
Author(s):  
Ayman Nagi ◽  
Meike Schroeder ◽  
Wolfgang Kersten
Keyword(s):  
Risk Management ◽  
Natural Disasters ◽  
Secondary Data ◽  
Community Analysis ◽  
Detection Algorithm ◽  
Structured Interviews ◽  
Emergency Plans ◽  
Plausibility Check ◽  
Safety Risks ◽  
Community Detection Algorithm

The aim of this work is to detect communities of stakeholders at the port of Hamburg regarding their communication intensity in activities related to risk management. An exploratory mixed-method design is chosen as a methodology based on a compact survey and semi-structured interviews, as well as secondary data. A compact survey at the port of Hamburg is utilized to address the communication intensity values among stakeholders. Based on 28 full responses, the data is extracted, cleansed, and prepared for the network analysis using the software “Gephi”. Thereafter, the Louvain community detection algorithm is used to extract the communities from the network. A plausibility check is carried out using 15 semi-structured interviews and secondary data to verify and refine the results of the community analysis. The results have revealed different communities for the following risk categories: (a) natural disasters and (b) operational and safety risks. The focus of cooperation is on the reactive process and emergency plans. For instance, emergency plans play an important role in the handling of natural disasters such as floods or extreme winds.

scholarly journals City consumption profile: a city perspective on the spending behavior of citizens

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Alessia Galdeman ◽  
Cheick T. Ba ◽  
Matteo Zignani ◽  
Christian Quadri ◽  
Sabrina Gaito
Keyword(s):  
Credit Card ◽  
Detection Algorithm ◽  
Economic Behavior ◽  
Single Type ◽  
Goods And Services ◽  
Usage Patterns ◽  
Spending Behavior ◽  
Community Detection Algorithm ◽  
The City ◽  
Consumption Profile

AbstractIn designing the city of the future, city managers and urban planners are driven by specific citizens’ behaviors. In fact, economic and financial behaviors, and specifically, which goods and services citizens purchase and how they allocate their spending, are playing a central role in planning targeted services. In this context, cashless payments provide an invaluable data source to identify such spending behaviors. In this work, we propose a methodology to extract the consumption behaviors of a large sample of customers through credit card transaction data. The main outcome of the methodology is a concise representation of the economic behavior of people residing in a city, the so-called city consumption profile. We inferred the city consumption profile from a network-based representation of the similarity among the customers in terms of purchase allocation; on top of which we applied a community detection algorithm to identify the representative consumption profiles. By applying the above methodology to a set of credit card transactions of an Italian financial group, we showed that cities, even geographically close, exhibit different profiles which makes them unique. Specifically, usage patterns focused on a single type of good/service—mono-categorical consumption profile—are the main factors leading to the differences in the city profiles. Our analysis also showed that there is a group of consumption profiles common to all cities, made up by purchases of primary goods/services, such as food or clothing. In general, the city consumption profile represents a tool for understanding the economic behaviors of the citizens and for comparing different cities. Moreover, city planners and managers may use it in the outline of city services tailored to the citizens’ needs.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

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