Identifying Key Resources in a Social Network using f-PageRank

Key resources are important, influential and powerful performers in a social network structure. Identifying them in the social network of a business process activity is beneficial and rewarding. One of the most effective centrality measures for identification of the key nodes in a social network is to rank resources based on a selection of criteria. PageRank is a representative example of such algorithms, which was first utilized in the Google search engine in 1998. However, the PageRank approach merely assumes a single link as a vote, which allows one originator to link or transfer his work to others more than once in a handover work scenario. We argue that this problem can lead to inaccurate influence based ranking in the context of business processes for resources in a social network. In this paper, we propose f-PageRank, a new approach specifically designed to identify the key resources in a social network generated from a business process activity. We evaluate our proposed method by comparing it with the existing approaches in process mining tools, such as degree centrality, betweenness centrality, BaryRanker, and HITS. The experimental results show that our approach can obtain a satisfying outcome.