# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 6 Conclusions  
The DGS framework is proposed in this paper as a novel method for summarizing large graph streams. To avoid the serious performance drops caused by collisions between hashes, DGS uses two separate matrices, the heavy sketch and the light sketch, to store heavy edges and light edges, respectively. In order to determine whether an edge is heavy or not, DGS first obtains node embeddings using a GAE, and adopts a DNN-based edge classifier which utilizes the node embeddings as features to make the classification. Edges classified as heavy edges will be stored in the heavy sketch, while edges classified as light edges will be stored in the light sketch. Furthermore, DGS sets a sample generator to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Wiki\_talk\_cy** & **Subelj\_jung** & **Facebook-Wosn-Wall** & **Slashdot** \\ \hline Memory usage & 4.19 KB & 54.18 KB & 342.57 KB & 54.99 KB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Memory usage comparison with respect to the datasets.  
Figure 8: Hyperparameter analysis on edge query task using dataset _subelj_jung_.  
Figure 9: Hyperparameter analysis on edge query task using dataset _slashdot_.  
periodically generate training samples for the edge classifier, and record the heavy edges of each session in the heavy node candidate list for fast top-k nodes query. In extensive experiments using four real-world graph streams, the proposed method was able to achieve high accuracy for graph queries including edge query and top-k nodes query compared to the state of the art. The ARE of edge query achieved by DGS was reduced by up to 24.19% compared to TCM, and was reduced by up to 60.88% compared to GSS. Meanwhile, the IA of node query achieved by DGS is significantly higher than that achieved by TCM. Finally, we made a hyperparameter analysis and found the best hyperparameters for the DGS framework.  
Conceptualization, D.L. and W.L.; methodology, D.L. and W.L.; software, D.L., Y.C. and X.Z.; validation, D.L. and M.L.; formal analysis, D.L.; investigation, D.L. and W.L.; resources, W.L. and S.L.; data curation, D.L. and G.Z.; writing--original draft preparation, D.L.; writing--review and editing, D.L. and W.L.; visualization, D.L., G.Z. and M.L.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant Numbers 62262018, 61972196.  
Not applicable.  
The datasets used in this article can be found at [[http://konect.c/networks/wiki_talk_cy/](http://konect.c/networks/wiki_talk_cy/)]([http://konect.c/networks/wiki_talk_cy/](http://konect.c/networks/wiki_talk_cy/)), [[http://konect.cc/networks/subeli_jung-j/](http://konect.cc/networks/subeli_jung-j/)]([http://konect.cc/networks/subeli_jung-j/](http://konect.cc/networks/subeli_jung-j/)), [[http://konect.cc/networks/facebook-wosn-wall/](http://konect.cc/networks/facebook-wosn-wall/)]([http://konect.cc/networks/facebook-wosn-wall/](http://konect.cc/networks/facebook-wosn-wall/)), and [[http://konect.cc/networks/slashot-threads/](http://konect.cc/networks/slashot-threads/)]([http://konect.cc/networks/slashot-threads/](http://konect.cc/networks/slashot-threads/)) (accessed on 10 December 2023).  
This work was partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.