Methods and systems for motif recognition in graph sets. Distribution scores comparing the canonical representations of a compressed graph in the set of graphs versus the canonical representations of a compressed graph in a reference graph are used to determine the significance of the compressed graph.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-20. (canceled) 21. A method for motif recognition in a set of graphs derived from a collection of noise reduced data, the method comprising: compressing an uncompressed graph from the set of graphs resulting in a compressed graph; counting the canonical representations of the compressed graph in the set of graphs to yield a first count; generating a reference graph from the noise reduced data, the reference graph being equal in size to the uncompressed graph; counting the canonical representations of the compressed graph in the reference graph to yield a second count; and computing a distribution score utilizing at least the first count and the second count to determine the significance of the compressed graph. 22. The method for motif recognition of claim 21, wherein compressing an uncompressed graph from the set of graphs comprises compressing the uncompressed graph using a g-trie data structure. 23. The method for motif recognition of claim 21, wherein the reference graph is a random graph or another uncompressed graph from the set of graphs. 24. The method for motif recognition of claim 21, wherein the canonical representation of the compressed graph is a vector form. 25. The method for motif recognition of claim 21, wherein counting the canonical representations of the compressed graph in the set of graphs to yield a first count comprises counting the canonical representations in a set of previously stored graphs. 26. The method for motif recognition of claim 21, wherein counting the canonical representations of the compressed graph in the random graph to yield the second count comprises counting the canonical representations of the compressed graph in one or more of an Endos-Renyi network and a Watts-Strogatz network. 27. The method for motif recognition of claim 21, further comprising classifying at least one individual user or at least one event based on the distribution score. 28. The method for motif recognition of claim 21, wherein the data is based on at least one of a user's actions over a period of time or a set of a user's most recent actions. 29. The method for motif recognition of claim 21, further comprising collecting data related to metadata of a user and data related to social interactions of a user. 30. The method for motif recognition of claim 29, further comprising computing features related to metadata of a user, features related to social interactions of a user, and features related to the motif of the user; and normalizing each feature on a zero to one scale. 31. The method for motif recognition of claim 30, further comprising aggregating each normalized feature into a numeric vector. 32. The method for motif recognition of claim 31, wherein the numeric vector is a training vector that is a positive vector that is representative of a class member or a negative vector that is not representative of a class member. 33. The method for motif recognition of claim 32, further comprising training a classification model using the training vector. 34. The method for motif recognition of claim 33, further comprising classifying, using the classification model, at least one other user associated with a new numeric vector. 35. The method for motif recognition of claim 21, further comprising forming a canonical representation using a greedy search. 36. The method for motif recognition of claim 21, wherein compressing an uncompressed graph from the set of graphs comprises compressing an uncompressed graph from the set of graphs to a size of 4. 37. An apparatus for performing the method of claim 21. 38. A method of classifying at least one social media user, the method comprising: detecting at least one motif of the social media user; gathering data related to metadata of the social media user; gathering data related to social interactions of the social media user; computing features of: the motif of the user, the data related to the metadata of the user, and the data related to the social interactions of the user; normalizing each computed feature on a zero to one scale; aggregating each normalized feature into a numeric vector; and comparing the numeric vector to at least one model vector, wherein the at least one model vector is representative of a class.