Graph-based reranking is effective for refining text-based video search results by making use of the social network structure. Unlike previous works which only focus on an individual video graph, the proposed method leverages the mutual reinforcement of heterogeneous graphs, such as videos and their associated tags obtained by social influence mining. Specifically, propagation of information relevancy across different modalities is performed by exchanging information of inter- and intra-relations among heterogeneous graphs. The proposed method then formulates the video search reranking as an optimization problem from the perspective of Bayesian framework. Furthermore, in order to model the consistency over the modified video graph topology, a local learning regularization with a social community detection scheme is introduced to the framework. Since videos within the same social community have strong semantic correlation, the consistency score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.