In today's world, social networks have become one of the primary means of connecting people around the world. These websites are home to vast amounts of data, with the volume of data growing at a remarkable pace. Social network data can be broadly classified into three main categories: user profile data, user communication data, and group communication data. To make sense of this massive and complex dataset, data mining techniques can be applied to discover knowledge and extract useful patterns. This process is known as social network mining. This paper proposes a new search protocol that can be used to mine information across all social networking data, and use the extracted patterns to search for experts in a particular field. The protocol includes a mechanism to rank the searched experts based on their relevance to the search criteria. Apart from identifying experts, this proposed protocol can also reveal a wealth of useful patterns from social networking data. This could be invaluable for research, marketing, or other applications that rely on insights derived from social network data