Effective Random Walk Models for Comparative Network Analysis

2020 ◽  
pp. 27-44
Author(s):  
Hyundoo Jeong
Keyword(s):  
Random Walk ◽  
Network Analysis ◽  
Comparative Network Analysis

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

Abstract 4912: Systematic, comparative network analysis on non-small cell lung cancer

2012 ◽  
Author(s):  
Serene Wong ◽  
Max Kotlyar ◽  
Dan Strumpf ◽  
Nick Cercone ◽  
Frances A. Shepherd ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Network Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Comparative Network Analysis

scholarly journals The vulnerability of plant-pollinator communities to honeybee decline: A comparative network analysis in different habitat types

2019 ◽  
Vol 97 ◽  
pp. 35-50 ◽  
Author(s):  
Anikó Kovács-Hostyánszki ◽  
Rita Földesi ◽  
András Báldi ◽  
Anett Endrédi ◽  
Ferenc Jordán
Keyword(s):  
Network Analysis ◽  
Habitat Types ◽  
Comparative Network Analysis ◽  
Plant Pollinator

scholarly journals Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis

PLoS ONE ◽  
2007 ◽  
Vol 2 (9) ◽  
pp. e881 ◽  
Author(s):  
Ketki D. Verkhedkar ◽  
Karthik Raman ◽  
Nagasuma R. Chandra ◽  
Saraswathi Vishveshwara
Keyword(s):  
Network Analysis ◽  
Comparative Network Analysis ◽  
Reaction Graphs
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