# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 2 Literature
LOCD identifies structural centers in a network based on their higher density and relative distance from nodes with higher densities. It expands communities around these centers using structural similarity. While LOCD effectively uncovers overlapping communities, the choice of cutoff distance and the use of structural similarity as a weighting measure can influence clustering results. Therefore, careful adjustment of these parameters is crucial to ensure accurate and reliable clustering outcomes. Moreover, k-Neighborhood Attribute Structural (kNAS) [56] is another technique for detecting overlapping clusters in a graph by considering structural and attribute similarities among vertices. It groups objects into clusters based on their k-nearest neighbors and similar attributes, enabling the identification of overlapping communities. The key parameter is the distance value (k). However, kNAS strictly satisfies both structural and attribute similarities but potentially does not provide a balanced trade-off. Link communities (LC) is a link-based method that detects communities as interrelated groups using hierarchical clustering and partition density optimization. It combines link-based analysis, hierarchical clustering, and partition density to identify overlapping communities and hierarchical organization in networks. The algorithm's results can be influenced by parameters like link weight threshold, community size threshold, resolution parameter, link strength measure, initialization strategy, convergence criteria, and network structure. Therefore, experimenting with different settings is crucial for achieving desired outcomes [57]. The extended link clustering (ELC) method improves the existing link clustering (LC) method by using extended link similarity (ELS) to create a denser transform matrix and employing EQ (an extended measure of quality of modularity) for optimal partitioning. This approach generates more realistic communities by incorporating more link information and using EQ to determine the cut level in the hierarchical clustering dendrogram. ELC achieves higher EQ values, making it more realistic compared to the original LC method and the classical CPM method [58]. Network Decomposition for Overlapping Community Detection (NDOCD) is a novel approach for overlapping community detection using network decomposition, node clustering, and seed expansion. It determines overlapping communities by applying a seed expansion strategy based on joint strength and membership degree thresholds. The approach showed superior performance compared to traditional link clustering and node clustering algorithms. However, its higher sensitivity is attributed to the adjustment of parameters like joint strength and membership degree [59]. The study in [60] introduces a Scalable Integrated Model using Game Theory (SIMGT), a novel approach for detecting overlapping communities in complex networks. It is inspired by social identity theory, and it models community formation as a noncooperative game, utilizing second-order proximity and stochastic gradient ascent. SIMGT proves effective, surpassing BigClam in AvgF1 across datasets, demonstrating scalability. Key parameters, including thresholds, proximity metrics (e.g., Jaccard coefficient), and weighting strategies, require careful optimization. These can be problematic for users in seeking optimal results. The GREESE algorithm [61] is introduced for overlapping community detection, employing a coupled-seeds expansion strategy, a fitness function, and a merging phase that regulates overlapping rates. It outperforms seven state-of-the-art algorithms in accuracy and execution time on diverse networks; GREESE offers simplicity, effectiveness, and promising results. The algorithm's parameters include Common Neighbor Similarity (C), Fitness Function (F), Expansion and Merging Phase Thresholds, as well as Network Parameters in LFR Benchmarks and Real-World Network Parameters. Optimal parameter selection may impact algorithm performance, requiring tuning for different networks.