Content authenticity and correctness is one of the important challenges in eLearning as there can be many solutions to one specific problem in cyber space. Therefore, the authors feel it is necessary to map problems to solutions using graph partition and weighted bipartite matching. This article proposes an efficient algorithm to partition question-answer (QA) space and explores the best possible solution to a particular problem. The approach described can be efficiently applied to social eLearning space where there are one-to-many and many-to-many relationships with a level of bonding. The main advantage of this approach is that it uses QA ranking by adjusted edge weights provided by subject-matter experts or the authors' expert database.