Summary: To solve the imbalance problem between the influence scope and the running time of the classic influence maximization algorithms, a mixed heuristic and greedy strategies based algorithm (MHG algorithm) for influence maximization in social networks is proposed. The algorithm considers the advantages on the greedy and heuristic strategies, and the selection of seed nodes is divided into two steps. First, the candidate node set is selected by the heuristic algorithm, and then the final seed node set is deduced from this set by the greedy algorithm. The results show that the MHG algorithm is superior in influence scope compared with current heuristic algorithms. It exhibits the approximate effect of the greedy algorithm, but the running time is obviously lower. Therefore, the MHG algorithm achieves a good balance between the influence scope and the running time. Moreover, the MHG algorithm presents a stable influence scope when running on real data sets and different dissemination models, showing its scalability in large scale social networks.