Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method. The source code of HSAN is shared at https://github.com/yueliu1999/HSAN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.

In the realm of infrared small-target detection, the weighted local contrast approaches, which seek to improve targets by the defined weighted factors, have garnered a lot of interest. However, there are several problems with these methods as follows. 1) The vast number of local contrast sliding sub-windows restricts the time efficiency. 2) The dim targets in the complicated backgrounds are incorrectly eliminated by the background suppression procedure. 3) The background noise in the complicated environment cannot be effectively muted. A simplified dual-weighted three-layer window local contrast method (SDWTLLCM) is suggested in this work as a solution to these issues. In order to extract tiny targets and suppress complicated backgrounds, a hierarchical convolution filtering window is first created. Then, even without sub-window division, a simple three-layer sliding window is created for time efficiency enhancement. The dual-weighted local contrast approach is also intended to minimize the background and further highlight tiny objects. Eventually, the tiny targets may be extracted more effectively using the adaptive threshold segmentation procedure. The vast experimental findings show that our suggested strategy is effective and efficient.