Table 3: Network-based subgroup analysis approaches in TCM.
Application and findings
Bron-Kerbosch algorithm: an efficient algorithm for finding all maximal cliques of a network. The recursive procedure for optimizing candidate selection is performed based on the three different sets (R, P, X) of nodes, where R represents the currently growing clique (initially empty), P denotes prospective nodes, and X stands for the nodes already processed .
Applied for the discovery of basic formula (BF) in herbal prescriptions of the famous TCM expert. Three BFs for psoriasis and four BFs for eczema were found .
A subnetwork detecting methods to find the required clusters in which all the nodes have at least k degree .
Applied for the subnetworks analysis of TCM ingredients target-target network, as well as for the measuring centrality of nodes by “ value” .
Applied for clustering symptoms for differentiating TCM syndrome of coronary heart disease based on the symptom-symptom network .
A network-based clustering algorithm to identify subgroups based on the new topological structure .
Applied for clustering functional proteins of PPIs network based on TCM cold and hot syndromes  or TCM therapy .
Clique percolation Method for finding such a subgroup that corresponds to fully connected k nodes .
Applied for detecting synergistic or antagonistic subgroups of clinical factors networks in TCM tumor treatment .
A simulated annealing algorithm, which is a generic probabilistic metaheuristic of the global optimizing for decomposing the networks .
Applied for subgroups detecting based on pathway-pathway association network for salvianolic acid B .