# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 4 Experimental Evaluation
### Effectiveness Evaluation  
In this section, we discuss about the effectiveness of our approach with respect to the state-of-the-art ones on two well-known datasets: BlogCatalog3 and Flickr. Firstly, we investigate the performance of the proposed framework when varying the percentage of edges, randomly chosen, setting \(s_{0}=1,2\), and \(3\) in Figure 8 to choose the maximum length of the relevant path. In particular, it is easy to note in Figure 8 that accuracy decreased when increasing the number of arcs removed, achieving better results when setting \(s_{0}=2\), while increasing the number of hops did not provide significant improvements in accuracy. Having defined their maximum length, we compared our approach with respect to different baselines on the BlogCatalog3 and Flickr datasets: _DeepWalk_[28], an iterative algorithm using local information obtained from a _random walk_ to learn latent representation, and _SDNE_[33], which relies on a deep model using a Laplacian eigenmap, _LINE_[34], embedding method using a stochastic gradient and negative samples.  
Figure 7: Loss analysis when varying the number of kernels. (**a**) Loss analysis with 2 convolutional layers and 3 kernels using gradient descent without decaying. (**b**) Loss analysis with 2 convolutional layers and 10 kernels using gradient descent without decaying.  
Furthermore, we evaluated our method also w.r.t. more recent approaches: the _GraphGAN_[29] framework, which is based on the union of generative and discriminative methods through adversarial training in a minimax game; _CANE_[30], which uses a community-aware network embedding together with adversarial learning; _DNNNC_[31], which exploits a deep node classification model, only relying on the network structure information.  
Tables 3 and 4 show the results of the comparison of our approach w.r.t. _SDNE_, _LINE_, _Deep Walk_, GraphGAN, CANE, and DNNNC on the BlogCatalog3 and Flickr datasets when varying the percentage of labeled nodes. It is easy to note that our approach was close to the best-case performance when the number of training instances increased.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **\% Labeled Nodes** & **10\%** & **30\%** & **60\%** & **90\%** \\ \hline \multirow{6}{*}{Micro-F1 (\%)} & CNN & 30.51 & 39.67 & 44.70 & 47.92 \\ \cline{2-6}  & DeepWalk & 36.00 & 39.60 & 41.30 & 42.00 \\ \cline{2-6}  & SDNE & 31.11 & 36.70 & 41.88 & 44.88 \\ \cline{2-6}  & LINE & 30.43 & 35.99 & 41.41 & 43.46 \\ \cline{2-6}  & GraphGAN & 28.78 & 39.01 & 42.71 & 44.76 \\ \cline{2-6}  & CANE & 28.99 & 39.21 & 42.98 & 45.01 \\ \cline{2-6}  & DNNNC & 28.15 & 38.91 & 41.89 & 44.59 \\ \hline \multirow{6}{*}{Macro-F1 (\%)} & CNN & 14.72 & 24.98 & 31.92 & 35.12 \\ \cline{2-6}  & DeepWalk & 21.30 & 25.30 & 27.60 & 28.90 \\ \cline{2-6}  & SDNE & 19.88 & 24.94 & 28.11 & 31.22 \\ \cline{2-6}  & LINE & 18.67 & 24.81 & 27.91 & 30.64 \\ \cline{2-6}  & GraphGAN & 17.88 & 23.54 & 29.87 & 33.01 \\ \cline{2-6}  & CANE & 18.12 & 23.85 & 30.04 & 33.42 \\ \cline{2-6}  & DNNNC & 17.55 & 23.21 & 29.45 & 32.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of our approach w.r.t. DeepWalk, SDNE, LINE, GraphGAN, CANE, and DNNNNC on the BlogCatalog3 dataset.  
Figure 8: Accuracy evaluation when removing different percentages of edges.