Trending topics represent the topics that are becoming increasingly popular and attract a sudden spike in human attention. Trending topics are critical and useful in modern search engines, which can not only enhance user engagements but also improve user search experiences. Large volumes of user search queries over time are indicative aggregated user interests and thus provide rich information for detecting trending topics. The topics derived from query logs can be naturally treated as a temporal correlation network, suggesting both local and global trending signals. The local signals represent the trending/non-trending information within each frequency sequence, and the global correlation signals denote the relationships across frequency sequences. We hypothesize that integrating local and global signals can benefit trending topic detection. In an attempt to jointly exploit the complementary information of local and global signals in temporal correlation networks, we propose a novel framework, Local-Global Ranking (LGRank), to both capture local temporal sequence representation with adversarial learning and model global sequence correlations simultaneously for trending topic detection. The experimental results on real-world datasets from a commercial search engine demonstrate the effectiveness of LGRank on detecting trending topics.