# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 2 Literature
However, the algorithm's scalability may be a limitation in large-scale network applications due to its reliance on a greedy expansion strategy. Nonetheless, GCE presents a valuable approach for capturing overlapping communities in social networks. The Overlapping Cluster Generator (OCG) method is a prominent approach for identifying overlap areas in protein-protein interaction (PPI) networks [47]. It employs a sophisticated methodology such as parameters like clique size, similarity threshold, and minimum cluster size to control the clustering process. By leveraging these parameters, OCG captures overlapping structures and identifies densely connected subgroups. However, OCG may be sensitive to parameter settings; hence, it requires tuning for optimal results. Another prominent algorithm, the improved Bacteria Foraging Optimization (IBFO) mechanism, combines the intuitionistic fuzzy set for identifying overlap modules in protein-protein interaction networks. This method effectively tackles the challenge of overlapping functional modules in PPI networks and automatically determines the number of clusters. However, the choice of parameter values, including the thresholds for membership degree and indeterminacy degree, as well as the maximum iterations of the IBFO algorithm, may significantly impact the resulting clustering outcomes [48]. The ETCC-SA (Edge-Triangle Clique Cover using Simulated Annealing) algorithm [49] is proposed for detecting overlapping communities in social networks. It explores different feature assignments for vertices and iteratively adjusts them based on a scoring function. A cluster is formed as the algorithm gradually converges toward an approximate solution. As the algorithm uses heuristic approximations, it does not guarantee optimality and is sensitive to parameter choices like the number of rounds and the mixing parameter. The Clique Overlap Newman-Girvan algorithm (CONGA) [50] is an extended version of the Girvan-Newman (GN) algorithm that specifically focuses on detecting overlapping clusters in networks. It enhances the GN algorithm by incorporating a mechanism to identify and represent overlapping communities. By considering the betweenness of edges and tracking overlapping vertices during the merging process, CONGA effectively detects and characterizes overlapping clusters in networks. Nevertheless, CONGA suffers from the resolution limit problem, making it unsuitable to detect smaller clusters within larger ones in overlapped communities. The Clique-Based Louvain Algorithm (CBLA) is an approach for detecting overlapping communities in graphs that combines concepts from the Clique Percolation Method (CPM) and the Louvain algorithm. It identifies cliques using CPM and then employs the Louvain algorithm to classify unclassified nodes. However, the Louvain algorithm has a resolution limit, posing difficulties in detecting communities at different scales and identifying smaller communities within larger ones [17; 51]. Furthermore, the CBLA's performance may be sensitive to parameter choices, including the clique size [52; 53]. A normalized cut-based scalable version of spectral clustering is proposed [54] to solve the problem of finding overlapping communities in large, complex networks. By utilizing a hierarchical framework and incorporating node weights, it addresses the challenge of identifying nodes belonging to multiple communities. However, the selection of the tolerance parameter within the hierarchical framework may significantly impact the clustering results. The algorithm can control the level of overlap between communities by adjusting the value of the threshold. A higher value allows for a greater degree of overlap, while a lower value results in less overlap. The selection of the optimal value requires careful consideration to achieve accurate and meaningful clustering results. In [55], a novel approach called LOCD (Local Overlapping Community Detection) is introduced to identify overlapping nodes within community structures.