Local community detection for a given set of query nodes attracts much research attention recently. The query nodes play essential roles in the detection effectiveness. Existing methods perform well when a query node is from the target community core region. However, they struggle with the query-bias issue and especially perform unsatisfactorily when the query nodes come from different communities or when certain query nodes are from communities overlapping region or community boundary region. To address above issues, we consider from a new angle, to replace these original 'intractable' query nodes with new detection-friendly query nodes. In this paper, we propose an effective ATP (Amplified Topology Potential) algorithm to detect core nodes of the target communities w.r.t. original query nodes. For one query node, ATP first builds a query-oriented topology potential field around the query node by aggregating random walk with restart scores. Then it amplifies the topology potential value to make core nodes of target communities easily distinguished. Graph-size-independent fast approximation strategies are also proposed together with sound theoretical foundations. Extensive experiments on four real networks using ten state-of-the-art local community detection methods verify the improvement in detection effectiveness and efficiency by the replacing strategy for the tough query cases.