Discovering the maximum k-clique on social networks using bat optimization algorithm

Computational Social Networks

Table 1 The summary of related work

Reference	Method	Pros	Cons
[27]	Genetic Algorithm	Reducing process time and cost	Small pattern tree and using more memory
[6]	Ant Colony Optimization	Searching large patterns without generating small or medium patterns	Hard to analyze and understand the algorithm
[19]	Heuristic Algorithm External Optimization	Good solutions and convergence speed	Low prediction confidence
[12]	Clustering	Usable in Internet marketing	Can be used only in IoT
[8, 8]	Edge centrality, optimization	Usable on large graphs	Need for expert intrusion
[4]	Statistical Methods	High precision and low classification cost	Hard to identify in unsupervised mode
[4]	Greedy Search	Low error	Computational Complexity
[5]	Basic Element Analysis	Better supervised modeling	Less effort on developing the model
[22]	Bit Pattern Search	Capability for using online	Use of pattern creation and testing
[26]	Decomposing Network to subgraphs	Identifying central or isolated users	Ignoring the time and computational complexity
[10]	Formal Analysis	Improving the F1 metric	Difficult to understand the logic of the proposed algorithm
[15]	Mathematical Analysis	Considering the time period	High computational time
[23]	Local Strategies	Reducing computational complexity	Ignoring performance metrics for comparison