scholarly journals Community Detection Using a Measure of Global Influence

2010 ◽  
pp. 20-35 ◽  
Author(s):  
Rumi Ghosh ◽  
Kristina Lerman
Keyword(s):  
Community Detection ◽  
Global Influence

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

2016 ◽  
Author(s):  
Ananth Kalyanaraman ◽  
Mahantesh Halappanavar ◽  
Daniel Chavarría-Miranda ◽  
Hao Lu ◽  
Karthi Duraisamy
Keyword(s):  
Community Detection

Kim Kardashian: the Leader of the Influencers

2019 ◽  
Author(s):  
Laila Fariha Zein ◽  
Adib Rifqi Setiawan
Keyword(s):  
Social Media ◽  
The United States ◽  
Armenian Genocide ◽  
The Public ◽  
Self Defense ◽  
Global Commerce ◽  
The World ◽  
Social Media Platforms ◽  
International Issues ◽  
Global Influence

In today’s world, it is easier and easier to stay connected with people who are halfway across the world. Social media and a globalizing economy have created new methods of business, trade and socialization resulting in vast amounts of communication and effecting global commerce. Like her or hate her, Kimberly Noel Kardashian West as known as Kim Kardashian has capitalized on social media platforms and the globalizing economy. Kim is known for two things: famous for doing nothing and infamous for a sex tape. But Kim has not let those things define her. With over 105 million Instagram followers and 57 million Twitter followers, Kim has become a major global influence. Kim has travelled around the world, utilizing the success she has had on social media to teach make-up master classes with professional make-up artist, Mario Dedivanovic. She owns or has licensed several different businesses including: an emoji app, a personal app, a gaming app, a cosmetics line, and a fragrance line. Not to be forgotten, the Kardashian family show, ‘Keeping Up with the Kardashians’ has been on the air for ten years with Kim at the forefront. Kim also has three books: ‘Kardashian Konfidential’, ‘Dollhouse’, and ‘Selfish’. With her rising social media following, Kim has used the platforms to show her support for politicians and causes, particularly, recognition of the Armenian genocide. Kim also recently spoke at the Forbes’ women’s summit. Following the summit, Kim tweeted out her support for a recent movement on Twitter, #freeCyntoiaBrown which advocated for a young woman who claimed to have shot and killed the man who held her captive as a teenage sex slave in self-defense. Kim had her own personal lawyers help out Cyntoia on her case. Kim has also moved beyond advocating for issues within the confines of the United States. As mentioned earlier, she is known for advocating for recognition of the Armenian genocide. In the last two years, her show has made it a point to address the Armenian situation as it was then and as it is now. Kim has been recognized as a global influencer by others across the wordl. We believe Kim has become the same as political leaders when it comes to influencing the public. Kim’s story reveals that the new reality creates a perfect opportunity for mass disturbances or for initiating mass support or mass disapproval. Although Kim is typically viewed for her significance to pop culture, Kim’s business and social media following have placed her deep into the mix of international commerce. As her businesses continue to grow and thrive, we may see more of her influence on international issues and an increase in the commerce from which her businesses benefit.

Research on community detection in social network

2014 ◽  
Vol 31 (1) ◽  
pp. 35
Author(s):  
Na Wang ◽  
Xia Li ◽  
Hongying Xu
Keyword(s):  
Social Network ◽  
Community Detection

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals Approaching the Optimal Solution of the Maximal α-quasi-clique Local Community Problem

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1438
Author(s):  
Patricia Conde-Cespedes
Keyword(s):  
Graph Theory ◽  
Community Detection ◽  
Local Community ◽  
Optimal Solution ◽  
Local Communities ◽  
Optimal Value ◽  
Interesting Task ◽  
Local Community Detection ◽  
Networks Analysis ◽  
Node Problem

Complex networks analysis (CNA) has attracted so much attention in the last few years. An interesting task in CNA complex network analysis is community detection. In this paper, we focus on Local Community Detection, which is the problem of detecting the community of a given node of interest in the whole network. Moreover, we study the problem of finding local communities of high density, known as α-quasi-cliques in graph theory (for high values of α in the interval ]0,1[). Unfortunately, the higher α is, the smaller the communities become. This led to the maximal α-quasi-clique community of a given node problem, which is, the problem of finding local communities that are α-quasi-cliques of maximal size. This problem is NP-hard, then, to approach the optimal solution, some heuristics exist. When α is high (>0.5) the diameter of a maximal α-quasi-clique is at most 2. Based on this property, we propose an algorithm to calculate an upper bound to approach the optimal solution. We evaluate our method in real networks and conclude that, in most cases, the bound is very accurate. Furthermore, for a real small network, the optimal value is exactly achieved in more than 80% of cases.

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