Enhanced email spam filtering through combining similarity graphs

Over the last decade Email Spam has evolved from being just an irritant to users to being truly dangerous. This has led web-mail providers and academic researchers to dedicate considerable resources towards tackling this problem 9, 21, 22, 24, 26. However, we argue that some aspects of the spam filtering problem are not handled appropriately in existing work. Principal among these are adversarial spammer efforts -- spammers routinely tune their spam emails to bypass spam-filters, and contaminate ground truth via fake HAM/SPAM votes -- and the scale and sparsity of the problem, which essentially precludes learning with a very large set of parameters.