Influence Diffusion, Community Detection, and Link Prediction in Social Network Analysis

2013 ◽  
pp. 305-325 ◽  
Author(s):  
Lidan Fan ◽  
Weili Wu ◽  
Zaixin Lu ◽  
Wen Xu ◽  
Ding-Zhu Du
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Link Prediction ◽  
Influence Diffusion

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.

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

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 Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

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 Taxonomy of Link Prediction for Social Network Analysis: A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183470-183487
Author(s):  
Herman Yuliansyah ◽  
Zulaiha Ali Othman ◽  
Azuraliza Abu Bakar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction
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