### Graph mining: Laws, generators, and algorithmsGraph mining: Laws, generators, and algorithms

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 Author Chakrabarti, Deepayan ♦ Faloutsos, Christos Source ACM Digital Library Content type Text Publisher Association for Computing Machinery (ACM) File Format PDF Copyright Year ©2006 Language English
 Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science Subject Keyword Generators ♦ Graphs ♦ Patterns ♦ Social networks Abstract How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any $\textit{M}$ : $\textit{N}$ relation in database terminology can be represented as a graph. A lot of these questions boil down to the following: “How can we generate synthetic but realistic graphs?” To answer this, we must first understand what patterns are common in real-world graphs and can thus be considered a mark of normality/realism. This survey give an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology, and computer science. Further, we briefly describe recent advances on some related and interesting graph problems. ISSN 03600300 Age Range 18 to 22 years ♦ above 22 year Educational Use Research Education Level UG and PG Learning Resource Type Article Publisher Date 2006-06-01 Publisher Place New York e-ISSN 15577341 Journal ACM Computing Surveys (CSUR) Volume Number 38 Issue Number 1

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Source: ACM Digital Library