Percolation Computation in Complex Networks

Reid, Fergal; McDaid, Aaron; Hurley, Neil

doi:10.1109/asonam.2012.54

Cited by 31 publications

(21 citation statements)

References 25 publications

(49 reference statements)

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“…LinkCommunities [23] is a method that clusters edges instead of nodes. CliquePerc [24,25] scans for the regions spanned by a rolling clique of certain size. Conclude [26] uses edge centrality distances to grow communities.…”

Section: B Community Detection Methodsmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

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Section: B Community Detection Methodsmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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“…Comparison with other detection methods. We compare our detection results with the results of a few wellknown algorithms for overlapping community detection, including the clique percolation (Perco) method [1,28], the link community (HLC) method [6], the SLPA algorithm [16], and the DEMON algorithm [14]; both SLPA and DEMON adopt the label propagation process, which our detection scheme relies on as well. Recommended parameters are used for these reference algorithms: for SLPA, the iteration timestep is 20 and r=0.1; for DEMON, ò DEMON =0.25 and the minimum community size is 3; for Perco, k=4 (4-clique); for HLC, the dendrogram is cut at the threshold of the maximum partition density for each experimented network (from left to right on the x-axis of figure 7, the thresholds are: 0.25, 0.29, 0.34, 0.33, 0.27, 0.48, 0.29, 0.21, 0.21, 0.20, 0.13).…”

Section: Resultsmentioning

confidence: 99%

“…SLPA is not deterministic, and detection results from multiple runs differ to a non-trivial extent; it is also not able to clearly separate random networks from real networks, at least by the number of communities detected as a fraction of the number of nodes (% of hubs), proportion of nodes that have multiple community memberships). Four well-known algorithms are studied besides our algorithm: clique percolation (Perco) [1,28], link community (HLC) [6], SLPA [16], and DEMON [14]. Perco and DEMON could not process properly on the sparse random network, and HLC did not generate result on the large-scale network (FB-artist); corresponding detection results are missing (NaN in the figure).…”

Section: Resultsmentioning

confidence: 99%

Self-falsifiable hierarchical detection of overlapping communities on social networks

Zhang

2020

New J. Phys.

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No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures.Community detection is a central topic in network science. Pioneered by works represented by Palla et al [1], in recent years more and more studies focus on the detection of overlapping communities as opposed to exhaustive communities, a division also regarded as soft-partitioning versus hard-partitioning. Besides relying on the optimization of certain metrics, e.g. 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 determina...

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“…CFinder employs the CPM (Reid et al , 2012) to detect functional modules. The basis for the CPM algorithm is the detection of maximal cliques from networks, which are combined according to their overlap; it detects k -cliques that share k − 1 nodes.…”

Section: Methodsmentioning

confidence: 99%

NCMine: Core-peripheral based functional module detection using near-clique mining

Tadaka

Kinoshita

2016

Bioinformatics

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Motivation: The identification of functional modules from protein–protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with “core” and “peripheral” proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level.Results: We propose NCMine, which is a novel network clustering method and visualization tool for the core-peripheral structure of functional modules. It extracts near-complete subgraphs from networks based on a node-weighting scheme using degree centrality, and reports subgroups as functional modules. We implemented this method as a plugin of Cytoscape, which is widely used to visualize and analyze biological networks. The plugin allows users to extract functional modules from PPI networks and interactively filter modules of interest. We applied the method to human PPI networks, and found several examples with the core-peripheral structure of modules that may be related to cancer development.Availability and Implementation: The Cytoscape plugin and tutorial are available at Cytoscape AppStore. (http://apps.cytoscape.org/apps/ncmine).Contact: kengo@ecei.tohoku.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

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Percolation Computation in Complex Networks

Cited by 31 publications

References 25 publications

Community detection in networks: Structural communities versus ground truth

Community detection in networks: Structural communities versus ground truth

Self-falsifiable hierarchical detection of overlapping communities on social networks

NCMine: Core-peripheral based functional module detection using near-clique mining

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