1. A computer-implemented method for modeling collusion detection, comprising: at a server computer in an enterprise computing environment: receiving historical click data from a client computer connected to the enterprise computing environment over a network connection, wherein the historical click data comprises a plurality of clicks generated over a period of time and information associated with the plurality of clicks, and wherein the information comprises visitor Internet Protocol (IP) address information, date and time information, publisher information, referrer information, user-agent information, searched keywords, and cookie; extracting entities of interest of one or more types from the historical click data; formulating potential collusion among the entities as a network problem, a vector space problem, or a combination thereof; wherein formulating potential collusion among the entities as a network problem comprises: constructing network representations of the entities and their relationships, wherein the entities are represented by nodes and wherein their relationships are represented by connections between the nodes; partitioning the network representations into subgroups of nodes to maximize a number of connections between the nodes in each subgroup; forwarding the subgroups to a network analyzer for producing a first set of potentially colluding entities; wherein formulating potential collusion among the entities as a vector space problem comprises: constructing vector space representations of the entities, wherein the vector space representations comprise vectors representing click patterns of the entities; grouping the vector space representations with similar anomalous patterns into clusters; and forwarding the clusters to a pattern analyzer for producing a second set of potentially colluding entities; and wherein formulating potential collusion among the entities as a combination of the network problem and the vector space problem comprises: transforming the subgroups of nodes from the network problem into vector spaces; and performing clustering on eigen vectors of the vector spaces to produce a third set of potentially colluding entities; and identifying, from the first set of potentially colluding entities, the second set of potentially colluding entities, or the third set of potentially colluding entities, one or more groups of entities having a degree of collusion corresponding to an organized activity on the Internet.