Social networks and statistical relational learning: a survey

One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people. With the spread of the Internet, several complex interactions have taken place among people, giving rise to huge Information Networks based on these interactions. Social Networks potentially represent an invaluable source of information that can be exploited for scientific and commercial purposes. On the other hand, due to their distinguishing peculiarities (huge size and inherent relational setting) with respect to all previous information extraction tasks faced in Computer Science, they require new techniques to gather this information. Social Network Mining (SNM) is the corresponding research area, aimed at extracting information about the network objects and behavior that cannot be obtained based on the explicit/implicit description of the objects alone, ignoring their explicit/implicit relationships. Statistical Relational Learning (SRL) is a very promising approach to SNM, since it combines expressive representation formalisms, able to model complex relational networks, with statistical methods able to handle uncertainty about objects and relations. This paper is a survey of some SRL formalisms and techniques adopted to solve some SNM tasks.