In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the Link Prediction Problem. This paper introduces the Missing Node Identification problem where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure.Towards solving this problem, we present the MISC Algorithm (Missing node Identification by Spectral Clustering), an approach based on a spectral clustering algorithm, combined with nodes' pairwise affinity measures which were adopted from link prediction research. We evaluate the performance of our approach in different problem settings and scenarios, using real life data from Facebook. The results show that our approach has beneficial results and can be effective in solving the Missing Node Identification Problem. In addition, this paper also presents R-MISC which uses a sparse matrix representation, efficient algorithms for calculating the nodes' pairwise affinity and a proprietary dimension reduction technique, to enable scaling the MISC algorithm to large networks of more than 100,000 nodes. Last, we consider problem settings where some of the indications are unknown. Two algorithms are suggested for this problem -Speculative MISC, based on MISC, and Missing Link Completion, based on classical link prediction literature. We show that Speculative MISC outperforms Missing Link Completion.

In recent years, social networks have surged in popularity as one of the main applications of the Internet. This has generated great interest in researching these networks by various fields in the scientific community. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on what connections were missing between nodes,or what is termed the "Missing Link Problem." This paper introduces a new Missing Nodes Identification problem where missing members in the social network structure must be identified. Towards solving this problem, we present an approach based on clustering algorithms combined with measures from missing link research. We show that this approach has beneficial results in the missing nodes identification process and we measure its performance in several different scenarios.