Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

It is observed that the confidence score may fail to reflect the predicting quality accurately in previous proposal-based line segment detection methods, since the scores and the line locations are predicted simultaneously. We find that the line segment detection performance can be further improved by learning-based line candidate ranking and optimizing strategy. To this end, we build a novel end-to-end line detecting model named RANK++LETR upon deformable DETR architecture, where the encoder is used to select the line candidates while the decoder is applied to rank and optimize these candidates. We design line-aware deformable attention (LADA) module in which attention positions are distributed in a long narrow area and can align well with the elongated geometry of line segments. Moreover, we innovatively apply ranking-based supervision in line segment detection task with the design of contiguous labels according to the detection quality. Experimental results demonstrate that our method outperforms previous SOTA methods in prediction accuracy and gets faster inferring speed than other Transformer-based methods.

Network alignment serves as a key methodology in data analysis, enabling a understanding of intricate structures within multiple networks. The discovery of potential aligned pairs between networks is a prerequisite for a wide spectrum of applications, e.g. cross-domain recommendation or re-identification attack. Presently, most existing network alignment methods involve only two networks, remaining challenges in scenarios with multiple networks. In addition, acquiring cross-network links often poses a significant challenge, resulting in numerous instances where priori annotation information is absent. Our research confronts these problems by proposing Multi-hop Transformation Multiple Network Alignment with Absent Priori Annotation (MT-MNA). The core idea is to simultaneously align multiple networks and address the scenarios of absent priori annotation by fully utilizing the rich-annotated network pairs. Through rigorous experiments on real-world datasets, our method demonstrates superior precision and adaptability to the challenging condition of absent priori annotation in multiple network alignment, overperforming existing state-of-the-art methods.

With the impressive performances achieved by graph representation learning models on tasks such as link prediction, their vulnerability to imperceptible adversarial perturbations has also come to light. However, adversarial attacks struggle to balance effectiveness and transferability, particularly when attempting to deliver effective attacks on various target models in one shot and achieving effective outcomes in both availability and integrity attack settings. This study explores a novel way to mitigate attack performance across various models under availability and integrity attack settings. To fulfill these objectives, we develop a Scoring & Update (SU) framework for performing adversarial attacks against link prediction on graphs. Specifically, we iteratively score perturbations through a parameter-frozen and transferable surrogate model and then update the perturbation with scoring feedback to learn a more effective and transferable adversarial perturbation. Extensive experiments on two real-world datasets show that our attack model is more effective than six attack baselines against six popular target models for graph representation under both availability and integrity attack settings. Code is available at https://github.com/anonymousaccept/STAA.

The expeditious advancement of social question-and-answer (Q&A) platforms has led to the valuable yet challenging practice of anonymous knowledge sharing. Despite implementing various anonymity techniques, the persistent threat of potential privacy breaches remains a paramount concern. To tackle this issue, we introduce the task of de-anonymization within Q&A communities and provide a bilingual dataset (Chinese and English) for research. In this paper, we propose a novel de-anonymization framework called MPT, effectively improving the model’s ability to capture long-range dependencies between nodes by integrating graph neural networks(GNNs) and language models(LMs). Specifically, we use GNN to extract structural features, and then we encode and fuse node representations from multiple meta-paths using Transformer and attention mechanisms. Extensive experiments on Zhihu and Quora data sets show that our model significantly outperforms the baseline model. In addition, our model possesses a degree of interpretability, enabling a comprehensive comprehension of the underlying factors contributing to user privacy breaches and facilitating the implementation of appropriate safeguards. The dataset1and code2utilized in this study have been made publicly accessible.