Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction

Šubelj, Lovro; Bajec, Marko

doi:10.1103/physreve.83.036103

Cited by 184 publications

(162 citation statements)

References 45 publications

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“…Using a data-driven community Louvain approach (13), cortical regions of interest (ROIs) were assigned to a cortical module, where each module represents a set of cortical ROIs that have maximum connections with each other but minimum connections with all other regions outside the module. This resulted in 6 modules, 3 in the left hemisphere and 3 in the right hemisphere.…”

Section: Resultsmentioning

confidence: 99%

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

McColgan

Seunarine²,

Gregory

et al. 2017

JCI Insight

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We lack a mechanistic explanation for the stereotyped pattern of white matter loss seen in Huntington’s disease (HD). While the earliest white matter changes are seen around the striatum, within the corpus callosum, and in the posterior white matter tracts, the order in which these changes occur and why these white matter connections are specifically vulnerable is unclear. Here, we use diffusion tractography in a longitudinal cohort of individuals yet to develop clinical symptoms of HD to identify a hierarchy of vulnerability, where the topological length of white matter connections between a brain area and its neighbors predicts the rate of atrophy over 24 months. This demonstrates a new principle underlying neurodegeneration in HD, whereby brain connections with the greatest topological length are the first to suffer damage that can account for the stereotyped pattern of white matter loss observed in premanifest HD.

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Section: Resultsmentioning

confidence: 99%

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

McColgan

Seunarine²,

Gregory

et al. 2017

JCI Insight

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“…The multilevel community (18) function of the igraph package (R/igraph 1.0.0; ) was utilized for network module mining. Screening of the modules containing >15 genes was used to perform differential expression analysis in the GSE16088 profile using the GlobalAncova package in R language (version 3.42.0; ).…”

Section: Methodsmentioning

confidence: 99%

Identification of characteristic gene modules of osteosarcoma using bioinformatics analysis indicates the possible molecular pathogenesis

Peng

et al. 2017

Molecular Medicine Reports

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The aim of the present study was to investigate the possible pathogenesis of osteosarcoma using bioinformatics analysis to examine gene-gene interactions. A total of three datasets associated with osteosarcoma were downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method, which then were subjected to the Human Protein Reference Database to identify the protein-protein interaction (PPI) pairs and to construct a PPI network of the DEGs. Subsequent multilevel community analysis was applied to mine the modules in the network, followed by screening of the differential expression module using the GlobalAncova package. The genes in the differential expression modules were verified in the valid datasets. The verified genes underwent functional and pathway enrichment analysis. A total of 616 DEGs were selected to construct the PPI network, which included 5,808 osteosarcoma-specific interaction pairs and 8,012 normal-specific pairs. Tumor protein p53 (TP53), mitogen-activated protein kinase 1 (MAPK1) and estrogen receptor 1 (ESR1) were identified the most important osteosarcoma-associated genes, with the highest levels of topological properties. Neurogenic locus notch homolog protein 3 (NOTCH3) and caspase 1 (CASP1) were identified as the osteosarcoma-specific interaction pairs. Among all 23 mined modules, three were identified as differential expression modules, which were verified in the other two datasets. The genes in these modules were predominantly enriched in the FGFR, MAPK and Notch signaling pathways. Therefore, TP53, MAPK1, ESR1, NOTCH3 and CASP1 may be important in the development of osteosarcoma, and provides valuable clues to investigate the pathogenesis of osteosarcoma using the three differential expression modules.

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“…Besides structure based method like CPM, overlapping community detection could also be modeled as link-partition problem [2,10,18]. It first converts the original graph G into link graph L(G), in which each node is a link of L(G), and two nodes are adjacent if the two links they represent have common node in G. Then link partition of G can be mapped to node partition of L(G), and by performing random walk [10], Jaccard-type similarity computation [2], or density-based clustering algorithm SCAN [18], node clusters of L(G) are derived and then they can be converted to overlapping node communities of G. Label propagation method has been also widely used for detecting communities [14,26], they propagate all nodes' labels to their neighbors for one step to make their community membership reach a consensus. Compared to OCD, OCS is more light-weight and flexible, it only needs to explore a part of the graph around the query nodes, but not the whole graph, thus it is more appropriate for online query.…”

Section: Related Workmentioning

confidence: 99%

Searching overlapping communities for group query

et al. 2015

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In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community detection, which takes the whole network as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing a given node is a flexible and light-weight solution, and also supports dynamic graphs very well. However, in some scenarios, it requires overlapping community search for group query, which means that the input is a set of nodes instead of one single node. To solve this problem, we propose an overlapping community search framework for group query, including both exact and heuristic solutions. The heuristic solution has four strategies, some of which are adjustable and self-adaptive. We propose two parameters node degree and discovery power to trade off the efficiency and quality of the heuristic strategies, in order to make This paper is an extended version of our previous conference paper [24]. World Wide Web them satisfy different application requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of both exact and heuristic solutions.

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Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction

Cited by 184 publications

References 45 publications

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

Identification of characteristic gene modules of osteosarcoma using bioinformatics analysis indicates the possible molecular pathogenesis

Searching overlapping communities for group query

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