An Anatomy of Bengaluru's ICT Cluster: A Community Detection Approach

2019 ◽  
Vol 15 (03) ◽  
pp. 533-561
Author(s):  
Ekaterina Turkina ◽  
Ari Van Assche
Keyword(s):  
Community Detection ◽  
Empirical Support ◽  
Community Formation ◽  
Community Discovery ◽  
Organizational Network ◽  
Detection Analysis ◽  
Detection Approach ◽  
Cluster A ◽  
Knowledge Seeking ◽  
Ict Cluster

ABSTRACTWe use community detection analysis to investigate the structure of Bengaluru's ICT cluster's inter-organizational network during the period 2015–2017. Building on the knowledge sourcing literature, we conjecture that cluster firms primarily build knowledge-seeking horizontal linkages with technologically similar companies, and that this splits the network into multiple technological communities within which firms are tightly connected, but between which linkages are scarce. We further propose that community-spanning firms which build horizontal linkages that bridge technological communities are more likely to conduct radical innovation than their peers. We finally argue that no relation exists between technological proximity and community formation in the network of vertical buyer-supplier relations. Using a voltage-based algorithm for community discovery, we draw empirical support for these predictions. We discuss the implications of our findings for Bengaluru's upgrading potential.

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

A new scalable leader-community detection approach for community detection in social networks

2018 ◽  
Vol 54 ◽  
pp. 41-49 ◽  
Author(s):  
Sara Ahajjam ◽  
Mohamed El Haddad ◽  
Hassan Badir
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Approach

scholarly journals Exploring inclusiveness towards immigrants as related to basic values: A network approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260624
Author(s):  
Hadi Sam Nariman ◽  
Lan Anh Nguyen Luu ◽  
Márton Hadarics
Keyword(s):  
Ethnic Groups ◽  
Community Detection ◽  
Latent Class ◽  
European Social Survey ◽  
Response Patterns ◽  
Social Survey ◽  
Detection Analysis ◽  
Network Properties ◽  
Global Connectivity ◽  
The Relationship

Using the 9th round of European Social Survey (ESS), we explored the relationship between Europeans’ basic values and their attitudes towards immigrants. Employing a latent class analysis (LCA), we classified the respondents based on three items capturing the extent to which participants would support allowing three groups of immigrants to enter and live in their countries: immigrants of same ethnic groups, immigrants of different ethnic groups, and immigrants from poorer countries outside Europe. Four classes of Europeans with mutually exclusive response patterns with respect to their inclusive attitudes towards immigrants were found. The classes were named Inclusive (highly inclusive), Some (selective), Few (highly selective), and Exclusive (highly exclusive). Next, using a network technique, a partial correlation network of 10 basic human values was estimated for each class of participants. The four networks were compared to each other based on three network properties namely: global connectivity, community detection, and assortativity coefficient. The global connectivity (the overall level of interconnections) between the 10 basic values was found to be mostly invariant across the four networks. However, results of the community detection analysis revealed a more complex value structure among the most inclusive class of Europeans. Further, according to the assortativity analysis, as expected, for the most inclusive Europeans, values with similar motivational backgrounds were found to be interconnected most strongly to one another. We further discussed the theoretical and practical implications of our findings.

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