  The introduction of the social networking platform has drastically affected
the way individuals interact. Even though most of the effects have been
positive, there exist some serious threats associated with the interactions on
a social networking website. A considerable proportion of the crimes that occur
are initiated through a social networking platform [5]. Almost 33% of the
crimes on the internet are initiated through a social networking website [5].
Moreover activities like spam messages create unnecessary traffic and might
affect the user base of a social networking platform. As a result preventing
interactions with malicious intent and spam activities becomes crucial. This
work attempts to detect the same in a social networking platform by considering
a social network as a weighted graph wherein each node, which represents an
individual in the social network, stores activities of other nodes with respect
to itself in an optimized format which is referred to as localized data-set.
The weights associated with the edges in the graph represent the trust
relationship between profiles. The weights of the edges along with the
localized data-set is used to infer whether nodes in the social network are
compromised and are performing spam or malicious activities.
