Malware detection and classification using community detection and social network analysis

2021 ◽  
Author(s):  
Varshini Reddy ◽  
Naimisha Kolli ◽  
N. Balakrishnan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Malware Detection ◽  
Malware Detection And Classification

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

scholarly journals Community detection and social network analysis based on the Italian wars of the 15th century

2020 ◽  
Vol 113 ◽  
pp. 25-40
Author(s):  
J. Fumanal-Idocin ◽  
A. Alonso-Betanzos ◽  
O. Cordón ◽  
H. Bustince ◽  
M. Minárová
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
15Th Century ◽  
Italian Wars

Community Detection Methods in Social Network Analysis

2014 ◽  
Vol 20 (1) ◽  
pp. 250-253 ◽  
Author(s):  
Andry Alamsyah ◽  
Budi Rahardjo ◽  
. Kuspriyanto
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Detection Methods

scholarly journals A Social Network Analysis of Football with Complex Networks

2019 ◽  
Author(s):  
Lucas G. S. Felix ◽  
Carlos M. Barbosa ◽  
Vinícius da F. Vieira ◽  
Carolina Ribeiro Xavier
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Complex Networks ◽  
Community Detection ◽  
Soccer Players ◽  
Main Function ◽  
Transfer Data ◽  
The World ◽  
Do So

Soccer is the most popular sport in the world and due its popularity, soccer moves billions of euros over the years, in most diverse forms, such as marketing, merchandising, TV quotas and players transfers. As example, in the 2016/2017 season, only England has moved about 1.3 billion of euros only in players transfers. In this work, it is performed a study of the transfer market of player. To do so, players transfer data were gathered from the website Transfermarkt and were modeled as a graph. In order to perform this study, different Complex Networks techniques were applied, such as Overlap Community Detection and Property Analysis. Through our results we could evaluate the soccer players market, and see a pattern that every market has at least one farm country, which has a main function of selling athletes, or a buyer country, which most of its transactions is buying players.

scholarly journals Detecting Communities in Science Blogs

2017 ◽  
Author(s):  
Christina Kirk Pikas
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Spin Glass ◽  
Community Detection ◽  
Online Communities ◽  
Subject Area ◽  
Analysis Methods ◽  
Science Blogs

Many scientists maintain blogs and participate in online communities through their blogs and other scientists' blogs. This study used social network analysis methods to locate and describe online communities in science blogs. The structure of the science blogosphere was examined using links between blogs in blogrolls and in comments. By blogroll, the blogs are densely connected and cohesive subgroups are not easily found. Using spin glass community detection, six cohesive subgroups loosely corresponding to subject area were found. By commenter links, the blogs form into more easily findable general subject area or interest clusters.

scholarly journals Data-driven modeling of diabetes care teams using social network analysis

2019 ◽  
Vol 26 (10) ◽  
pp. 911-919 ◽  
Author(s):  
Mina Ostovari ◽  
Charlotte-Joy Steele-Morris ◽  
Paul M Griffin ◽  
Denny Yu
Keyword(s):  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Health Care Providers ◽  
Centrality Measures ◽  
Care Providers ◽  
Multi Scale ◽  
Number Of Patients

Abstract Objective We assess working relationships and collaborations within and between diabetes health care provider teams using social network analysis and a multi-scale community detection. Materials and Methods Retrospective analysis of claims data from a large employer over 2 years was performed. The study cohort contained 827 patients diagnosed with diabetes. The cohort received care from 2567 and 2541 health care providers in the first and second year, respectively. Social network analysis was used to identify networks of health care providers involved in the care of patients with diabetes. A multi-scale community detection was applied to the network to identify groups of health care providers more densely connected. Social network analysis metrics identified influential providers for the overall network and for each community of providers. Results Centrality measures identified medical laboratories and mail-order pharmacies as the central providers for the 2 years. Seventy-six percent of the detected communities included primary care physicians, and 97% of the communities included specialists. Pharmacists were detected as central providers in 24% of the communities. Discussion Social network analysis measures identified the central providers in the network of diabetes health care providers. These providers could be considered as influencers in the network that could enhance the implication of promotion programs through their access to a large number of patients and providers. Conclusion The proposed framework provides multi-scale metrics for assessing care team relationships. These metrics can be used by implementation experts to identify influential providers for care interventions and by health service researchers to determine impact of team relationships on patient outcomes.

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