Genetic clustering of social networks using random walks

2007 ◽  
Vol 51 (12) ◽  
pp. 6285-6294 ◽  
Author(s):  
Aykut Firat ◽  
Sangit Chatterjee ◽  
Mustafa Yilmaz
Keyword(s):  
Social Networks ◽  
Random Walks ◽  
Genetic Clustering

scholarly journals Faster Random Walks by Rewiring Online Social Networks On-the-Fly

2016 ◽  
Vol 40 (4) ◽  
pp. 1-36 ◽  
Author(s):  
Zhuojie Zhou ◽  
Nan Zhang ◽  
Zhiguo Gong ◽  
Gautam Das
Keyword(s):  
Social Networks ◽  
Random Walks ◽  
Online Social Networks

Random Walks in Social Networks and their Applications: A Survey

2011 ◽  
pp. 43-77 ◽  
Author(s):  
Purnamrita Sarkar ◽  
Andrew W. Moore
Keyword(s):  
Social Networks ◽  
Random Walks

Scalable Method for Information Spread Control in Social Networks

2018 ◽  
pp. 220-240
Author(s):  
Michal Wojtasiewicz ◽  
Mieczysław Kłopotek
Keyword(s):  
Social Networks ◽  
Cluster Analysis ◽  
Random Walks ◽  
Signal Propagation ◽  
Control Signal ◽  
Information Spread ◽  
Information Spreading

In this chapter, scalable and parallelized method for cluster analysis based on random walks is presented. The aim of the algorithm introduced in this chapter is to detect dense sub graphs (clusters) and sparse sub graphs (bridges) which are responsible for information spreading among found clusters. The algorithm is sensitive to the uncertainty involved in assignment of vertices. It distinguishes groups of nodes that form sparse clusters. These groups are mostly located in places crucial for information spreading so one can control signal propagation between separated dense sub graphs by using algorithm provided in this work. Authors have also proposed new coefficient which measures quality of given clustering with respect to information spread control between clusters. Measures presented in this paper can be used for determining quality of whole partitioning or a single bridge.

scholarly journals A Method for Improving the Accuracy of Link Prediction Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Jie Li ◽  
Xiyang Peng ◽  
Jian Wang ◽  
Na Zhao
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Random Walks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Decomposition Method ◽  
Local Information ◽  
Evolution Mechanism ◽  
Real Network ◽  
Prediction Algorithms

Link prediction is a key tool for studying the structure and evolution mechanism of complex networks. Recommending new friend relationships through accurate link prediction is one of the important factors in the evolution, development, and popularization of social networks. At present, scholars have proposed many link prediction algorithms based on the similarity of local information and random walks. These algorithms help identify actual missing and false links in various networks. However, the prediction results significantly differ in networks with various structures, and the prediction accuracy is low. This study proposes a method for improving the accuracy of link prediction. Before link prediction, k-shell decomposition method is used to layer the network, and the nodes that are in 1-shell and the nodes that are not linked to the high-shell in the 2-shell are deleted. The experiments on four real network datasets verify the effectiveness of the proposed method.

Non-homogeneous Random Walks

2016 ◽  
Author(s):  
Mikhail Menshikov ◽  
Serguei Popov ◽  
Andrew Wade
Keyword(s):  
Random Walks
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