As a popular similarity measurement, Graph Edit Distance (GED) can be applied to various downstream tasks. Traditional GED solvers have difficulty in generalization and time efficiency, so deep graph similarity learning models have emerged as a promising trend. However, existing methods lack distinguishing embeddings for cross-dependencies between graphs and intra-graph representations. In this paper, we propose a deep network architecture GED-CDA by introducing a graph-graph context dependency attention module to enhance embeddings. Our graph-graph context dependency attention module consists of a cross-attention layer and a self-attention layer, which can both learn the interactions between graph pairs and capture the connections within graphs. In addition, we also introduce a spectral encoding strategy to bring topological information of graphs into node representations. Extensive experimental results on three real-world graph datasets demonstrate the effectiveness of our proposed GED-CDA in the GED task.

Deriving from image matching and understanding, semantic keypoint matching aims at establishing correspondence between keypoint sets in images. As graphs are powerful tools to represent points and their complex relationships, graph matching provides an effective way to find desired semantic keypoint correspondences. Recent deep graph matching methods have shown excellent performance, but there is still a lack of exploration and utilization of spatial information of keypoints as nodes in graphs. More specifically, existing methods are insufficient to capture the relative spatial relations through current graph construction approaches from the locations of semantic keypoints. To address these issues, we introduce a positional reconstruction encoder-decoder (PR-EnDec) to model intrinsic graph spatial structure, and present an end-to-end graph matching network PREGM based on PR-EnDec. Our PR-EnDec consists of a positional encoder that learns effective node spatial embedding with the affine transformation invariance, and a spatial relation decoder that further utilizes the high-order spatial information by reconstructing the locational structure of graphs contained in the node coordinates. Extensive experimental results on three public keypoint matching datasets demonstrate the effectiveness of our proposed PREGM.

Continual graph learning (CGL) aims to mitigate the topological-feature-induced catastrophic forgetting problem (TCF) in graph neural networks, which plays an essential role in the field of information retrieval. The TCF is mainly caused by the forgetting of node features of old tasks and the forgetting of topological features shared by old and new tasks. Existing CGL methods do not pay enough attention to the forgetting of topological features shared between different tasks. In this paper, we propose a transformer-based CGL method (Trans-CGL), thereby taking full advantage of the transformer's properties to mitigate the TCF problem. Specifically, to alleviate forgetting of node features, we introduce a gated attention mechanism for Trans-CGL based on parameter isolation that allows the model to be independent of each other when learning old and new tasks. Furthermore, to address the forgetting of shared parameters that store topological information between different tasks, we propose an asymmetric mask attention regularization module to constrain the shared attention parameters ensuring that the shared topological information is preserved. Comparative experiments show that the method achieves competitive performance on four real-world datasets.

The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.

Molecular image retrieval is one of the crucial steps in automatic mining and utilization of biochemistry-related literatures, which is also a relatively open and challenging task in cross fields of biochemistry and artificial intelligence. The challenges come from two aspects: 1) there is a lack of open datasets and evaluation criteria for molecular image retrieval. 2) Common retrieval methods always ignore that molecular image retrieval has cross-modal information of both images and SMILES texts. To address the first challenge, we firstly construct a new molecular image retrieval benchmark, named MIR, including 130770 molecular images, labeled structural similarity, and reasonable evaluation metrics. Faced with the second challenge, we propose an effective cross-modal pre-training framework for molecular image retrieval following CLIP. Experimental results reflect the effectiveness of our proposed benchmark MIR and cross-modal pre-training framework.