scholarly journals Network structure optimization for social networks by minimizing the average path length

2021 ◽  
Author(s):  
Wei Du ◽  
Gang Li ◽  
Xiaochen He
Keyword(s):  
Social Sciences ◽  
Network Structure ◽  
Path Length ◽  
Memetic Algorithm ◽  
Average Path Length ◽  
Major Research ◽  
Research Focus ◽  
Network Diameter ◽  
Network Structure Optimization ◽  
Definition Of

Abstract Network structure plays an important role in the natural and social sciences. Optimization of network structure in achieving specified goals has been a major research focus. In this paper, we propose a definition of structural optimization in terms of minimizing the network’s average path length (APL) by adding edges. We suggest a memetic algorithm to find the minimum-APL solution by adding edges. Experiments show that the proposed algorithm can solve this problem efficiently. Further, we find that APL will ultimately decrease linearly in the process of adding edges, which is affected by the network diameter.

scholarly journals Optimization of the Critical Diameter and Average Path Length of Social Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Haifeng Du ◽  
Xiaochen He ◽  
Wei Du ◽  
Marcus W. Feldman
Keyword(s):  
Social Networks ◽  
Dynamic Optimization ◽  
Path Length ◽  
Memetic Algorithm ◽  
Critical Diameter ◽  
Mathematic Model ◽  
Average Path Length ◽  
Experimental Results ◽  
Network Diameter ◽  
The Relationship

Optimizing average path length (APL) by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL.

scholarly journals Fat-Burning or Fat-Shaming? Theme and Community Overlap Between Online, Weight-Stigmatizing and Exercise-Promoting Networks.

2021 ◽  
Author(s):  
Ahuitz Rojas-Sánchez ◽  
Jenine K. Harris ◽  
Philippe Sarrazin ◽  
Aïna Chalabaev
Keyword(s):  
Social Media ◽  
Weight Loss ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Structure ◽  
Path Length ◽  
Average Path Length ◽  
Common Word ◽  
Total Degree

Abstract Purpose: This study aimed to determine if networks of users consistently posting about exercise and fat exist and overlap on social media sites.Method: We collected 3,772,507 posts from Twitter that included the words “fat” and “exercise”. Using network structure methods, we identified communities of interconnected users and overlaps between those tweeting “fat” and those tweeting “exercise”. Results: Common word pairings were identified using Natural Language Processing (NLP). Networks of users consistently talking about exercise (n=3,573) and fat (n=2,007) were found on Twitter. An increased mean total-degree and reduced average path length indicate that the fitness-talk network serves as a connecting bridge between highly scattered communities of the weight-talk network. Conclusion: We identified groups on Twitter dedicated to consistently producing weight stigmatizing content and promoting exercise with weight-loss messages. These groups partially overlap with pro-health groups which could lead to users looking for exercise advice in Twitter to find themselves immersed in a stigmatizing network.

scholarly journals Loneliness is negatively related to Facebook network size, but not related to Facebook network structure

2018 ◽  
Author(s):  
Riana M. Brown ◽  
Sam G. B. Roberts ◽  
Thomas Victor Pollet
Keyword(s):  
Network Structure ◽  
Path Length ◽  
Large Body ◽  
Network Size ◽  
Individual Characteristics ◽  
Average Path Length ◽  
Physical And Mental Health ◽  
Number Of Clusters ◽  
Size Number ◽  
Study Participants

High levels of loneliness are associated with poorer outcomes for physical and mental health and a large body of research has examined how using social media sites such as Facebook are associated with loneliness. Time spent on Facebook tends to be associated with higher levels of loneliness, whereas a larger number of Facebook Friends and more active use of Facebook tends to be associated with lower levels of loneliness. However, whilst the network size and structure of ‘offline’ networks have been associated with loneliness, how the network structure on Facebook is associated with loneliness is still unclear. In this study, participants used the Getnet app to directly extract information on network size (number of Facebook Friends), density, number of clusters in the network, and average path length from their Facebook networks, and completed the 20-item UCLA Loneliness questionnaire. In total, 107 participants (36 men, 71 women, mean age = 20.6, SD = 2.7) took part in the study. Participants with a larger network size reported significantly lower feelings of loneliness. In contrast, network density, number of clusters, and average path length were not significantly related to loneliness. These results suggest that whilst having a larger Facebook network is related to feelings of social connection to others, the structure of the Facebook network may be a less important determinant of loneliness than other factors such as active or passive use of Facebook and individual characteristics of Facebook users.

scholarly journals Loneliness is negatively related to Facebook network size, but not related to Facebook network structure

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Riana M. Brown ◽  
Sam G. B. Roberts ◽  
Thomas V. Pollet
Keyword(s):  
Network Structure ◽  
Path Length ◽  
Large Body ◽  
Network Size ◽  
Individual Characteristics ◽  
Average Path Length ◽  
Physical And Mental Health ◽  
Number Of Clusters ◽  
Size Number ◽  
Study Participants

High levels of loneliness are associated with poorer outcomes for physical and mental health and a large body of research has examined how using social media sites such as Facebook is associated with loneliness. Time spent on Facebook tends to be associated with higher levels of loneliness, whereas a larger number of Facebook Friends and more active use of Facebook tends to be associated with lower levels of loneliness. However, whilst the network size and structure of ‘offline’ networks have been associated with loneliness, how the network structure on Facebook is associated with loneliness is still unclear. In this study, participants used the Getnet app to directly extract information on network size (number of Facebook Friends), density, number of clusters in the network, and average path length from their Facebook networks, and completed the 20-item UCLA Loneliness questionnaire. In total, 107 participants (36 men, 71 women, Mage = 20.6, SDage = 2.7) took part in the study. Participants with a larger network size reported significantly lower feelings of loneliness. In contrast, network density, number of clusters, and average path length were not significantly related to loneliness. These results suggest that whilst having a larger Facebook network is related to feelings of social connection to others, the structure of the Facebook network may be a less important determinant of loneliness than other factors such as active or passive use of Facebook and individual characteristics of Facebook users.

C2, Networks, and Self-Synchronization

2014 ◽  
pp. 191-215 ◽  
Author(s):  
A. H. Dekker
Keyword(s):  
Experimental Data ◽  
Network Topology ◽  
Network Structure ◽  
Network Theory ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Scale Free ◽  
Node Connectivity ◽  
Network Properties

This chapter examines the connection between network theory and C2, particularly as it relates to self-synchronization, which requires a rich network structure. The richness of the network can be measured by the average degree, the average path length, and the average node connectivity. The chapter explores the connection between these measures and the speed of self-synchronization, together with other network properties, which can affect self-synchronization, resilience, and responsiveness. Two important network structures (random and scale-free) are described in the context of self-synchronization. Experimental data relating network topology to self-synchronization speed is also explored. In particular, the chapter notes the connection between average path length and self-synchronization speed, as well as the importance of good networking between sub-networks.

scholarly journals The Influence of Alliance Innovation Network Structure upon Enterprise Innovation: A Case Study of China’s Energy-Saving and Environment-protection Industry

2018 ◽  
Vol 13 (2) ◽  
pp. 208
Author(s):  
Meng Xu
Keyword(s):  
Energy Saving ◽  
Network Structure ◽  
Betweenness Centrality ◽  
Path Length ◽  
Clique Number ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Environment Protection ◽  
Different Types ◽  
Enterprise Innovation

The Energy-saving and environment-protection industry, an important strategic and emerging industry in China, will develop into a pillar industry. In view of global climate change, environmental pollution, resource depletion and the defects and deficiencies in traditional technology, technology and product innovation constitute the lifeline of energy-saving and environment-protection industry. The alliance network of enterprises will influence, stimulate, and regulate enterprise innovation greatly. A comprehensive analysis of alliance data of China's energy-saving and environment-protection industry from 2000 to 2013 by using Ucinet software can reveal the network structure parameters such as degree, clique number, average path length, clustering coefficient, and betweenness centrality, which reflects different types of enterprise networks and different positions of enterprises in different types of networks. A negative regression analysis of enterprise patent data and network structure parameters by using Stata software can make some conclusions that the influences of network characteristics on enterprise innovation reach the maximum in the second year of the window period end, that innovation accumulation, clustering coefficient, betweenness centrality are related to the enterprise innovation, that clique number, network density are negatively related to the enterprise innovation, and that there is an inverted U relationship between average path length and enterprise innovation. It is suggested to increase the accumulation level of innovation, appropriately control the network density, reduce the average path length, improve the betweenness centrality and clustering coefficient of enterprises, so as to improve the overall innovation level. 

From Fieldwork to Research Theory on an Indian Pilgrimage

2005 ◽  
Vol 1 (2) ◽  
pp. 105-121 ◽  
Author(s):  
Rémy Delage
Keyword(s):  
Social Sciences ◽  
Random Sampling ◽  
South India ◽  
Scientific Discourse ◽  
The Other ◽  
Critical Reading ◽  
Major Research ◽  
Research Hypothesis ◽  
Sources Of Knowledge ◽  
The One

Using as the example of the pilgrimage to Sabarimala (Kerala, South India), I propose here to explore the links existing between sources, research hypothesis and research theory in social sciences. The choice of research materials in the process of investigation, sources of knowledge about the studied object, is not mere random sampling; it is processed in accordance with the questions of the researcher. It inevitably assumes a selective dimension. After a critical reading of the sources used by Indian studies, I will highlight on the connections between the sources and the methodological tools on the one hand, and the major research hypothesis about pilgrimage on the other. The links between the data taken from the field and the legitimacy of scientific discourse on India will be examined at the end before providing some keys for the interpretation of Sabarimala phenomenon in South India during the contemporary period.

Islamic Science

1993 ◽  
Vol 10 (3) ◽  
pp. 305-311
Author(s):  
Sirajul Husain
Keyword(s):  
Social Sciences ◽  
Islamic Science ◽  
Definition Of ◽  
Empirical Methodology ◽  
Human Inquiry

The term "Islamic science" can be defined as the scientific way of definingand comborating the uniquely monotheistic concept of tuwhfd(unity), a concept that can serve as an epistemological manifold for intellectualinquiry and development. In this context, science is taken as a systematicway of looking at things or, in other words, as both a philosophyof knowledge as well as an empirical methodology. When taken in its entirety,science includes the whole spectrum of human inquiry rangingfrom ontology to epistemology, from causality to cosmology, and fromthe natural and social sciences to technology. It may be noted that beyondan axiomatic application based on a metaphysical definition of tawhid,there has been no scientific attempt to analyze and substantiate thisconcept. This axiomatic application of tawhid, especially when dealing withan analysis of developments in knowledge, raises certain epistemologicalquestions. As it does not scientifically define or discuss the verypremise-tawhid-on which the analysis is being based, this is to be expected.Furthermore, for example, the axiomatic application of tuwhid topurge the corpus of knowledge of its secular elements and then reconstructit within the tawhidi framework cannot be fulfilled, as it is unableto furnish a tawhid-based scientific temperament without first providingscientific combomtion of the concept itself. It is from such an epistemologicalviewpoint that we find the contributions of Muslims to variousfields of leaming tend to be more sentimental than scientific.The need to develop Islamic science also arises from the fact thatmost modem scientists are known to be secular, as they have consciouslyevaded the issue of the existence of a Creator. This is the result of their ...

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

scholarly journals Topological measures for identifying and predicting the spread of complex contagions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas Guilbeault ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Shortest Path ◽  
Empirical Data ◽  
Path Length ◽  
Centrality Measures ◽  
Social Phenomena ◽  
Node Centrality ◽  
Network Connectedness ◽  
Topological Measures ◽  
Definition Of

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

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