## 3 Approaches
### Graph-Based  
Graph-based spam reviews detection is a process of mathematically representing the network and their relationship through lines and points to describe the connection of the reviews entities. That is to say, the graph-based approach is a branch of data mining techniques that depend on the inter-dependencies between objects. This approach is used to study the patterns of the relations of a network in order to distinguish the hidden patterns. In our case, graph data of the spam reviews (fraudulent) rely on the rating of the reviewer  
Figure 3: Ensemble learning architecture. for a product and the other reviewers' rate for that product. While the non-spam reviews (trustworthy) rely, somehow, on different products that they rated. Therefore, graph-based detection introduced a robust mechanism for capturing and identifying the relationships and correlations of data objects inter-dependent. For instance, [PERSON]. [155] provided a novel graph-based spam detection model to recognize the reliability of reviews. The paper compared various methods for the sake of proving the efficiency of the proposed model. Different attack scenarios are taken into account when examining the model. The results indicate that the proposed model identifies the spam reviews in different scenarios efficiently. Moreover, it can reduce the trust value of the spam review as well as prevent spammers from ruining the reputation of the product. [PERSON] et al. [156] used a deep dynamic clustering model to detect spam reviews by utilizing graph embedding structure to preserve the nonlinear information structure of the text. The authors also applied dynamic aspects of reviewers in order to determine the spam users from the normal ones. In the phase of the experiment, the results show that the spam detection model reached 92%. While [157], stated that the current spam reviews detection techniques used one to two kinds of entities only alongside employing a few types of features, namely content, relation, and behavior features. However, such techniques suffer from not being able to be employed in context and tend more to synthetic criteria. Therefore, a new graph-based model is presented to detect spam reviews. The model (Multi-iterative Graph-based opinion Spam Detection (MGSD) is utilized with all classes of entities as well as a unified structure. Afterward, both the implicit and explicit relation is used, then an evaluation of the Spamicity effects is applied. In order to improve the performance of the detection model number of weighted features were taken into account. The outcome of the experiments indicated the superior of the proposed model with 93% and 95.3% for the synthetic dataset and [PERSON]'s dataset, respectively. Further, [PERSON]. [158] declared that the previous works in the literature consider several reviews as a whole when performing feature extraction, at the same time, disregarding the internal differences and similarities. Thus, causes to not identify the most discriminative information due to semantics reviews disorder. To solve such a problem, they proposed a co-attention framework and orthogonal model for the decomposition process. Hence, learn the differences and similarities between the reviews. Also, to distinguish the strong social connection, they determined first the weak relation graph through the dynamic interactions method. Subsequently, graph representation is applied to learn the interaction of social connections. Their framework outperforms the state-of-the-art methods evaluated on two real-world datasets. Whereas, the authors of [159] designed a community detection technique (SC-Com) for spam reviews detection. SC-Com used the graph of reviewers for splitting the communities based on their reciprocal doubt. Then a temporal abnormality and community-based features extraction are employed for identifying the spam reviews from non-spam. Using a real-world dataset, an evaluation phase is done and the presented approach achieves the highest results utilizing ratings and time data.