# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 4 Experiment  
This section will show the effectiveness of the MCEP through experiments. In this paper, five representative algorithms are chosen as baselines. The five algorithms are CFCR [10], SGCI [8], PATOC [9], node2 vec [22], and LINE [23]. For the first two algorithms, since the author did not give the abbreviations of the algorithm, for the convenience of subsequent presentation, we have named these two algorithms. In addition, since the representation of the community can also be obtained through graph representation learning, we use two well-known graph representation learning algorithms as comparison algorithms; these are node2 vec and LINE. To prevent confusion with the original algorithm, we use Cnode2 vec and CLINE to represent the corresponding algorithms. To use these two algorithms for community evolution prediction, after obtaining the node representation, we calculate the average value of the node representation of each community. The average value is the feature corresponding to the community; we can use these features to make predictions.  
We conducted experiments on three real datasets, which are DBLP [24], ia-reality-call [[https://networkrepository.com/ia-reality-call.php](https://networkrepository.com/ia-reality-call.php)]([https://networkrepository.com/ia-reality-call.php](https://networkrepository.com/ia-reality-call.php)) (accessed on 12 March 2022), and sx-askubuntu-c2q[[http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz](http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz)]([http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz](http://snap.stanford.edu/data/sx-askubuntu-c2q.txt.gz)) (accessed on 12 March 2022). Table 3 shows the dataset used in the experiment. As can be seen from Table 3, the three networks are small, medium and large, respectively. In addition, the types of these three networks are also different, where DBLP is a citation network, ia-reality-call is a mobile phone network, and sx-askubuntu-c2q is a question-answering network. Since networks vary in size and type, we believe using these three datasets is sufficient to demonstrate the effectiveness of our proposed algorithm.  
All algorithms use logistic regression as the classifier. To increase the number of community events, ia-reality-call has one snapshot overlap, and sx-askubuntu-c2q has two snapshots overlap. To ensure the stability of the experimental results, the experiment used ten-fold cross-validation and was repeated 20 times; each time, the order of the data was randomly shuffled. The final experimental result was an average of 20 times. The experiment uses accuracy as a measure.  
Since the main difference of the comparison algorithm lies in feature selection, to ensure a fair comparison, all algorithms use the same steps in the first three steps of community evolution prediction. The algorithm used in the community discovery part is LFM [25]. The algorithm used in the evolution analysis part is GED [17]. The two parameters in the GED algorithm are both 0.5. Since the final training set has the problem of imbalance between positive and negative samples, we use SMOTE [26] to balance the positive and negative samples before training the classifier.