Abstract: A system and method for predicting victims and detecting fake accounts in OSNs, comprising: a feature-based classifier for predicting victims by classifying, with a classification probability, a target variable of each user in the OSN social graph; a graph-transformer for transforming the social graph into a defense graph by reassigning edge weights to incorporate victim predictions using the classification probability, a graph-based detector for detecting fake users by computing through the power iteration method a probability of a random walk to land on each node in the defense graph after 0(log n) steps, assigning to each node a rank value equal to a node's landing probability normalized by the node degree, sorting the nodes by their rank value and estimating a detection threshold such that each node whose rank value is smaller than the detection threshold is flagged as representing a fake account.

Abstract: A method and a system for analyzing dynamic graphs are disclosed. In accordance with such method and system, computations are performed at a plurality of graph vertices every time a change in the graph occurs. In order to minimize the computational load of each computation iteration, previous computation results are reused when the inputs for a computation at a given vertex are unchanged from previous computations. This approach enables real-time data mining from large dynamic graphs, without requiring users to devise their own incremental graph algorithms.

Abstract: The invention discloses a method of monitoring a bittorrent network and measuring download speeds. The speed is calculated by connecting to a client, getting the bitfield and have messages, reconnecting after a predetermined time and getting a new bitfield and have messages. Taking the number of new pieces between both observations multiplied by the piece size and divided by the time between the two observations the download speed is obtained.