Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Table 1

Summary of methods for collusive spamming group detection.

Category	Method	Data modeling	Candidate group discovery method	Spamming behavior evaluation	Detection method
Group content and behavior analysis-based methods	Mukherjee et al. [3]	Construct behavioral and relation models	Frequent itemset mining	The average of spam indicators	Ranking
	Xu et al. [4]	Measure the pairwise similarity of groups	Frequent itemset mining	No	KNN-based method
	Xu and Zhang [7]	Use pairwise features to model the relations among colluders	No	The weighted sum of all the pairwise features	Ranking
	Xu and Zhang [8]	Use homogeneity-based behavior features to model the collusive review fraud	Frequent itemset mining	Similarity-based measure	Unified probabilistic model
	Zhang et al. [9]	Use a number of features to model the group spamming behavior	Frequent itemset mining	No	Semi-supervised classification
Graph-based methods	Ye and Akoglu [10]	Model the dataset as a bipartite graph	No	Reviewers’ network footprint scores	Hierarchical clustering
	Choo et al. [11]	Model the dataset as a user relationship graph	No	No	Community-based method
	Wang et al. [12]	Model the dataset as a bipartite graph	Graph partitioning	The average of spam indicators	Ranking
	Do et al. [13]	Model the dataset as a review graph	No	The average of spam indicators	k-means clustering
	Do et al. [14]	Model the dataset as a review graph	No	The average of spam indicators	Fuzzy k-means clustering
	Han et al. [15]	Model the dataset as a user relationship graph	No	No	Graph spectrum analysis
	Wang et al. [16]	Model the dataset as a user relationship graph	Graph partitioning	The average of spam indicators	Ranking
	Cao et al. [21]	Model the dataset as a bipartite graph	No	The average of spam indicators	Hierarchical agglomerative clustering
	Zhang et al. [22]	Model the dataset as a user relationship graph	Label propagation	Linearly weighted sum of spam indicators	Ranking