TowerDNA: Fast and Accurate Graph Retrieval With Dividing, Contrasting and Alignment

Junwei Yang, Yiyang Gu, Yifang Qin, Xiao Luo, Zhiping Xiao, Kangjie Zheng, Wei Ju, Xian-Sheng Hua, Ming Zhang

Abstract: Graph retrieval (GR), a ranking procedure that aims to sort the graphs in a database by their relevance to a query graph in decreasing order, has wide applications across diverse domains, such as visual object detection and drug discovery. Existing Graph Retrieval (GR) approaches usually compare graph pairs at a detailed level and generate quadratic similarity scores. In realistic scenarios, conducting quadratic fine-grained comparisons is costly. However, coarse-grained comparisons would result in performance loss. Moreover, label scarcity in real-world data brings extra challenges. To tackle these issues, we investigate a more realistic GR problem, namely, efficient graph retrieval (EGR). Our key intuition is that, since there are numerous underutilized unlabeled pairs in realistic scenarios, by leveraging the additional information they provide, we can achieve speed-up while simplifying the model without sacrificing performance. Following our intuition, we propose an efficient model called Dual-Tower Model with Dividing, Contrasting and Alignment (TowerDNA). TowerDNA utilizes a GNN-based dual-tower model as a backbone to quickly compare graph pairs in a coarse-grained manner. In addition, to effectively utilize unlabeled pairs, TowerDNA first identifies confident pairs from unlabeled pairs to expand labeled datasets. It then learns from remaining unconfident pairs via graph contrastive learning with geometric correspondence. To integrate all semantics with reduced biases, TowerDNA generates prototypes using labeled pairs, which are aligned within both confident and unconfident pairs. Extensive experiments on diverse realistic datasets demonstrate that TowerDNA achieves comparable performance to fine-grained methods while providing a 10× speed-up.

External IDs:dblp:journals/tkde/YangGQLXZJHZ26