Graph mining: Laws, generators, and algorithms Export

@article{chakrabarti:graph, 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 M : 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.}, address = {New York, NY, USA}, author = {Chakrabarti, Deepayan and Faloutsos, Christos}, citeulike-article-id = {2408026}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1132952.1132954}, citeulike-linkout-1 = {http://dx.doi.org/http://doi.acm.org/10.1145/1132952.1132954}, doi = {http://doi.acm.org/10.1145/1132952.1132954}, issn = {0360-0300}, journal = {ACM Comput. Surv.}, keywords = {data-mining, graph-theory, social-network-analysis, survey}, number = {1}, pages = {2+}, posted-at = {2009-08-06 22:27:00}, priority = {2}, publisher = {ACM}, title = {Graph mining: Laws, generators, and algorithms}, url = {http://dx.doi.org/http://doi.acm.org/10.1145/1132952.1132954}, volume = {38}, year = {2006} }

TY - JOUR ID - chakrabarti:graph L3 - citeulike-article-id:2408026 N2 - 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 M : 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. IS - 1 JF - ACM Comput. Surv. SN - 0360-0300 AD - New York, NY, USA TI - Graph mining: Laws, generators, and algorithms PB - ACM VL - 38 SP - 2 KW - data-mining KW - graph-theory KW - social-network-analysis KW - survey AU - Chakrabarti, Deepayan AU - Faloutsos, Christos PY - 2006/// UR - http://dx.doi.org/http://doi.acm.org/10.1145/1132952.1132954 ER -