Table 3 Influential nodes finding approaches comparison
From: Influential nodes identification in complex networks: a comprehensive literature review
References | Approach | Network type | Network nature | Network direction | Network size | Implementation datasets |
|---|---|---|---|---|---|---|
[76] | HKS | Unweighted/weighted | Static | Undirected | All | LFR, ZBR, ZKC, CTG, DLP, CPF,NTS, ELG, ERD, CCG, HMS, UG, PGP, ASP, ENR |
[69] | Coreness centrality (Cnc) | Unweighted | Dynamic | Undirected | All | ZKC, DLP, JZ,ELG,NTS, URV, BG, UG, BA, LFR PG, ASP, CA-CondMat, ENR, EM |
[59] | Kshell decomposition | Weighted/Unweighted | Dynamic | Directed/undirected | Medium and large | LJ, EM, CNI, IMDB, CondMat RL, AS, PS |
[68] | Mixed degree decomposition (MDD) | Unweighted | Static | Undirected | All | DLP, JZ, NTS, EM, Ca-HepTh, PGP, ASP CondMat, WAN, ECP, ELG, TAP, Y2H, PWR, Int |
[66] | k-shell iteration factor (KS-IF) | Unweighted | Dynamic | Undirected | All | LFR,ZKC,DLP, JZ, NTS, EM, BG, PGP, ENR,FB, TW |
[77] | Eigenvector centrality | Unweighted | Static | Directed | Small | JPEF |
[57] | PageRank | Unweighted/weighted | Dynamic | Directed | Large | Google search Engine |
[58] | LeaderRank | Unweighted | Static | Directed | Large | DLC |
[60] | HITS | Weighted/unweighted | Dynamic | Directed | Small | Clever search engine |
[78] | TOPSIS | Unweighted | Static | Undirected, directed | Medium and large | UP, AL, EM, ACF |
[17] | W-TOPSIS | Unweighted | Static | Undirected | Large | YST, BG, RTR, PGP |
[63] | AHP | Unweighted | Static | Undirected | Medium and large | EM, GRD, YST, UP |
[64] | LS-SVM | Unweighted/Weighted | static | Undirected/directed | All | WS small-world network, power-law, BA scale-free network, UP, DLP,ACF, NTS, EM |
[73] | infGCN | Unweighted | Static | Undirected | Large | HMS, HP, CA-GrQc, CA-HepTh, CondMat |