1. A method for identifying a threatening network, the method being implemented by a computer system and comprising: providing a dataset comprising network transaction data of a plurality of networks; performing an AT-SIG algorithm on the dataset; and displaying a graphic output of the AT-SIG algorithm for each of the plurality of networks, wherein the AT-SIG algorithm comprises providing a network movement before/after algorithm that provides a graphical plot of changes in network transaction activity from before to after a specified time, wherein the network movement before/after algorithm, after accepting the specified time, accepting a selection of one or more metrics of interest, and accepting a selection of a time interval duration, performs the steps of: 1) assigning weights to edges between pairs of nodes in a network equal to the average frequency of transactions between the pairs of nodes as the pairs of nodes appear in the time interval duration; 2) randomly sampling from Poisson distribution of the edges to create a sample of each of the networks, and computing a plurality of metrics for the networks to generate a matrix that is N×M in size, wherein N is the cardinality of the plurality of networks and M is the cardinality of the plurality of the metrics of interest; and 3) repeating steps 1) to 2) multiple times to generate multiple samples for the metrics of interest for each network, wherein the network movement before/after algorithm generates a set of samples of metrics of interest for each network before the specified time and a set of samples of metrics of interest for each network after the specified time; wherein the AT-SIG algorithm further comprises one or more of the following: providing a network progression algorithm that provides a graphical plot of analyze behavior in small increments of time without specification or emphasis upon a particular time or event; providing a statistical network anomaly ranking algorithm that provides as output a ranked list of the network; and providing an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time.