Malware Detection via Extended Label Propagation Through Graph Inference

File Relation Graph Based Malware Detection Using Label Propagation

Polonium: Tera-Scale Graph Mining and Inference for Malware Detection

We present Polonium, a novel Symantec technology that detects malware through large-scale graph inference. Based on the scalable Belief Propagation algorithm, Polonium infers every file’s reputation, flagging files with low reputation as malware. We evaluated Polonium with a billion-node graph constructed from the largest file submissions dataset ever published (60 terabytes). Polonium attained...

Multi Label Text Classification through Label Propagation

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...

Community detection in dynamic networks via adaptive label propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation pr...

Bootstrapping via Graph Propagation

Bootstrapping a classifier from a small set of seed rules can be viewed as the propagation of labels between examples via features shared between them. This paper introduces a novel variant of the Yarowsky algorithm based on this view. It is a bootstrapping learning method which uses a graph propagation algorithm with a well defined objective function. The experimental results show that our pro...