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.