A community discovery algorithm based on boundary nodes and label propagation

NSLPCD: Topic based tweets clustering using Node significance based label propagation community detection algorithm

“…Liu et al [45] proposed the edge label propagation algorithm (ELPA) by combining the link community with the execution efficiency of the LPA. Gui et al [46] proposed the label boundary node algorithm (LBN) that handles the random update process in the label propagation to improve the stability of the algorithm. Our proposed approach -NSLPCD handles the randomness nature of the LPA algorithm to improve the quality performance of detected communities.…”

Self-falsifiable hierarchical detection of overlapping communities on social networks

“…Gfeller et al [32] regarded them as 'unstable nodes' and discussed the determination of these nodes through essentially a Monte-Carlo approach by imposing random noises on edge weights. Gui et al [33] proposed a seed-set-based label propagation algorithm that discovers 'boundary nodes' as opposed to 'core nodes', whose basic idea is similar. Sun et al [34] also discussed the 'hubs' and 'outliers' among 'homeless' nodes identified in the detection process.…”

“…modularity [2], conductance [3], fitness [4], or aggregative metric based on link prediction algorithms [5] etc, as the extensively-studied traditional approach for detecting exhaustive communities, for overlapping communities, a lot of new tools are designed based on various ideas, including link communities [6, 7], clique percolation [8], seed set expansion [3,[9][10][11][12], label propagation [13][14][15][16], local spectral clustering method [17], and methods based on statistical inference, such as Infomap [18], the stochastic block model (SBM,[19,20]; in particular, methods adopting the belief propagation algorithm [21,22]), and other generative models [23,24].Despite the success of different solution schemes on various application fronts, some weak points of existing community detection algorithms could be pinned down in practice, which we believe might be problematic in certain cases (see appendix A for a detailed discussion). These weaknesses include: (1) many solution schemes are over-parameterized, and in some cases the tuning of parameters depends largely on unwarranted heuristics;(2) many scalable methods based on the seed set expansion process [2-4, 9, 25-28] may lack well-designed seeding strategies [10,11,29] and often rely on ad-hoc strategies; (3) some algorithms that claim to be local, as opposed to methods based on an optimization over the entire graph, in fact still optimize on the community level and thus do not guarantee complete locality; (4) the number of communities in the graph is often predetermined in certain algorithms, which might not be a good treatment, despite its claimed advantage [30] and the possible determination by the non-backtracking matrix [31]; (5) the overlapping communities revealed by some algorithms are in fact still exhaustive in their corresponding link communities [6], which should not be an implicit constraint imposed by algorithms; (6) in many cases, the revealed communities do not follow any order and instead are treated as of equal significance to the graph ('blended' [30]), which may deviate from realistic situations; (7) most algorithms assume that all nodes in the graph should belong to at least one community, without taking care of those isolated nodes that do not have any community membership [32][33][34]…”

“…(2) many scalable methods based on the seed set expansion process [2-4, 9, 25-28] may lack well-designed seeding strategies [10,11,29] and often rely on ad-hoc strategies; (3) some algorithms that claim to be local, as opposed to methods based on an optimization over the entire graph, in fact still optimize on the community level and thus do not guarantee complete locality; (4) the number of communities in the graph is often predetermined in certain algorithms, which might not be a good treatment, despite its claimed advantage [30] and the possible determination by the non-backtracking matrix [31]; (5) the overlapping communities revealed by some algorithms are in fact still exhaustive in their corresponding link communities [6], which should not be an implicit constraint imposed by algorithms; (6) in many cases, the revealed communities do not follow any order and instead are treated as of equal significance to the graph ('blended' [30]), which may deviate from realistic situations; (7) most algorithms assume that all nodes in the graph should belong to at least one community, without taking care of those isolated nodes that do not have any community membership [32][33][34][35]; (8) finally, a OPEN ACCESS RECEIVED…”

An adaptive hybrid algorithm for social networks to choose groups with independent members

“…After that, they detect the community based on their similarity. Also in [7], a new algorithm was proposed to remove mostly random selection and reduces the iteration times and keeps the original time efficiency.…”