An Efficient Algorithm for Optimizing Bipartite Modularity in Bipartite Networks

Liu, Xin; Murata, Tsuyoshi

doi:10.20965/jaciii.2010.p0408

Cited by 36 publications

(24 citation statements)

References 27 publications

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“…After this first analysis, we computed modularity within each resulting module independently to test for the occurrence of submodules inside modules. As a sensitivity test, we also analysed modularity using the recently implemented LPAb+ algorithm (Beckett, ; Liu & Murata, ) (see Appendix S2).…”

Section: Methodsmentioning

confidence: 99%

Spatial distance and climate determine modularity in a cross‐biomes plant–hummingbird interaction network in Brazil

Araujo

González

Sandel

et al. 2018

Journal of Biogeography

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Aim We examined the effects of space, climate, phylogeny and species traits on module composition in a cross‐biomes plant–hummingbird network. Location Brazil, except Amazonian region. Methods We compiled 31 local binary plant–hummingbird networks, combining them into one cross‐biomes metanetwork. We conducted a modularity analysis and tested the relationship between species’ module membership with traits, geographical location, climatic conditions and range sizes, employing random forest models. We fitted reduced models containing groups of related variables (climatic, spatial, phylogenetic, traits) and combinations of groups to partition the variance explained by these sets into unique and shared components. Results The Brazilian cross‐biomes network was composed of 479 plant and 42 hummingbird species, and showed significant modularity. The resulting six modules conformed well to vegetation domains. Only plant traits, not hummingbird traits, differed between modules, notably plants’ growth form, corolla length, flower shape and colour. Some modules included plant species with very restricted distributions, whereas others encompassed more widespread ones. Widespread hummingbirds were the most connected, both within and between modules, whereas widespread plants were the most connected between modules. Among traits, only nectar concentration had a weak effect on among‐module connectivity. Main conclusions Climate and spatial filters were the main determinants of module composition for hummingbirds and plants, potentially related to resource seasonality, especially for hummingbirds. Historical dispersal‐linked contingency, or environmental variations not accounted for by the explanatory factors here evaluated, could also contribute to the spatial component. Phylogeny and morphological traits had no unique effects on the assignment of species to modules. Widespread species showed higher within‐ and/or among‐module connectivity, indicating their key role connecting biomes, and, in the case of hummingbirds, communities within biomes. Our results indicate that biogeography and climate not only determine the variation of modularity in local plant–animal networks, as previously shown, but also affect the cross‐biomes network structure.

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Section: Methodsmentioning

confidence: 99%

Spatial distance and climate determine modularity in a cross‐biomes plant–hummingbird interaction network in Brazil

Araujo

González

Sandel

et al. 2018

Journal of Biogeography

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“…Significant TTC signals were discretized as binary values, and Barber's modularity was maximized which identifies two-mode networks of disjoint gene sets such that interaction only occurs with genes of another brain region [27]. LPAb+ algorithm outperforms other methods for bipartite networks [28,29], and we utilized its two-stage procedure where first "bottom-up" step propagates labels iteratively to maximize node-by-node modularity and second "top-down" step joins modules together to increase network modularity [30]. Different random initialization of node selection was performed five times for all 136 ROI pairs and confirmed that the maximized modularity converged to same optimal solution.…”

Section: Bipartite Clusteringmentioning

confidence: 99%

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease

Woo

Roussos

Haroutunian

et al. 2020

BMC Med

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Background: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. Methods: Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. Results: We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10 −9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (P permutation = 1.0 × 10 −2 and 4.9 × 10 −4 for left and right respectively).

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“…Barber and Clark [33] generalized it (LPAb) to optimize bipartite modularity. Liu and Murata [20,21] further introduced an improved label propagation algorithm (LPAb+) for this task. However, both LPAb and LPAb+ are designed for maximizing Barber's bipartite modularity.…”

Section: Bilpa For Bipartite Community Partitionmentioning

confidence: 99%

“…In bipartite networks, nodes are divided into two disjoint sets (i.e., a bipartite network is composed of two types of nodes), and only two nodes from different sets can be connected. In the past several years, community detection in bipartite networks has attracted great interests as well [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Quantitative function and algorithm for community detection in bipartite networks

Wang

Zhang

et al. 2016

Information Sciences

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Community detection in complex networks is a topic of high interest in many fields. Bipartite networks are a special type of complex networks in which nodes are decomposed into two disjoint sets, and only nodes between the two sets can be connected. Bipartite networks represent diverse interaction patterns in many real-world systems, such as predator-prey networks, plant-pollinator networks, and drug-target networks. While community detection in unipartite networks has been extensively studied in the past decade, identification of modules or communities in bipartite networks is still in its early stage. Several quantitative functions proposed for evaluating the quality of bipartite network divisions are based on null models and have distinct resolution limits. In this paper, we propose a new quantitative function for community detection in bipartite networks, and demonstrate that this quantitative function is superior to the widely used Barber's bipartite modularity and other functions. Based on the new quantitative function, the bipartite network community detection problem is formulated into an integer programming model. Bipartite networks can be partitioned into reasonable overlapping communities by maximizing the quantitative function. We further develop a heuristic and adapted label propagation algorithm (BiLPA) to optimize the quantitative function in large-scale bipartite networks. BiLPA does not require any prior knowledge about the number of communities in the networks. We apply BiLPA to both artificial networks and real-world networks and demonstrate that this method can successfully identify the community structures of bipartite networks.

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An Efficient Algorithm for Optimizing Bipartite Modularity in Bipartite Networks

Cited by 36 publications

References 27 publications

Spatial distance and climate determine modularity in a cross‐biomes plant–hummingbird interaction network in Brazil

Spatial distance and climate determine modularity in a cross‐biomes plant–hummingbird interaction network in Brazil

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease

Quantitative function and algorithm for community detection in bipartite networks

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