Update

README.md

@@ -28,27 +28,59 @@ An official source code for paper Hard Sample Aware Network for Contrastive Deep
 ### Overview
 
 <p align = "justify"> 
-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.
+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.
 <div  align="center">    
     <img src="./assets/HSAN_model.png" width=80%/>
 </div>
 
+
 <div  align="center">    
       Figure 1: Illustration of the proposed Hard Sample Aware Network (HSAN).
 </div>
 
+### Requirements
+
+
 
 
 
+### Quick Start
+
+The code will be released soon. 
 
 
 
+### Clustering Result
+
+<div  align="center">    
+    <img src="./assets/HSAN_result.png" width=100%/>
+</div>
+
+<div  align="center">    
+    Table 1: Clustering results of our proposed HSAN and thirteen baselines on six datasets.
+</div>
+
+
+
+
+<div  align="center">    
+    <img src="./assets/HSAN_tsne.png" width=100%/>
+</div>
+
+<div  align="center">    
+    Figure 2: 2D <i>t</i>-SNE visualization of seven methods on two datasets.
+</div>
+
+### Citation
+
+```
+
+```
 
 
 
 
 
 
-The code will be released soon.