Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk

Jiao, Qingju; Huang, Yan; Shen, Hong‐Bin

doi:10.1016/j.physa.2015.01.022

Cited by 8 publications

(8 citation statements)

References 57 publications

(81 reference statements)

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“…In order to detect multi-scale modular structures of functional modules and protein complexes in biological networks, we employed our defined ISIM node similarity metric 22 and hierarchical clustering to solve these problems. In ISIM, we proposed a modified transition probability matrix from node i to node j on a network using a constrained random walk.…”

Section: Methodsmentioning

confidence: 99%

“…However, the parameter value of 0.95 in RWR is widely used in some literature studies, 27,28 and this value is also employed to detect modules in ISIM. 22 Therefore, in this section, the parameters in the three node similarity metrics were set at 0.97, 0.95 and a neutral value of 0.5. In fact, the evaluation of the robustness of different metrics will not be affected by the parameter, because what we will compare is the self-difference of each similarity metric on the five versions of PPI networks.…”

Section: Robustness Of Isim To Biological Network Data Qualitymentioning

confidence: 99%

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A new multi-scale method to reveal hierarchical modular structures in biological networks

2016

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Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

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Section: Methodsmentioning

confidence: 99%

Section: Robustness Of Isim To Biological Network Data Qualitymentioning

confidence: 99%

A new multi-scale method to reveal hierarchical modular structures in biological networks

2016

Self Cite

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“…At the same time, five single-scale community detection methods are introduced to detect the communities in the above two synthetic and four real-world networks, including the improved subspace iteration method (ISIM) [1], the Infomap [16], greedy modularity optimization method [3], Louvain [17] and OSLOM [18]. The detection results were compared with the metadata by normalized mutual information [19] (NMI):…”

Section: Single-scale Community Detectionmentioning

confidence: 99%

“…Many community detection methods have been developed for different types of networks. These approaches are based on either global or local topologies [1].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale View Reveals Easily Detectable Community in Complex Networks

Jiao¹,

Jin²

2019

ISI

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Community is a prominent feature of complex networks, and many methods have emerged for community detection. However, the existing methods have many intrinsic drawbacks and cannot work effectively. To solve the problems, this paper probes deep into the effects of multiand single-scale community detection methods. Firstly, a typical multi-scale method was adopted to detect the communities in synthetic and real-world networks. Then, five singlescale methods were employed for community detection in the same networks. The detection results of both types of methods were analyzed in details. The results show that, for a given network, the communities must be prominent and easily detectable by single-scale methods, if these communities are both strongly synchronous and if the synchronous partition falls within the stable partitions detected by multi-scale methods. If these communities only satisfy the first condition, then they can be detected by some special single-scale methods. If these communities are asynchronous, then they cannot be effectively detected by single-scale methods, and should be treated with multi-scale approaches. The research findings shed new light on the design of community detection methods.

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“…community, has triggered a big activity to this field [1][2][3], which is considered to be capable of revealing the network structure. Many methods have been put forward to detect network communities, such as modularity [4] and its variations [5][6][7], and hierarchical clustering methods based on node [8] or edge similarity [9].…”

Section: Introductionmentioning

confidence: 99%

Selection of Significant Community Structure Based on Network Partition-based Cluster

Jiao¹,

Jin²

2019

JESA

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Module (or community) structure detection, which has been successfully applied to many fields, is vital step for understand network dynamic and complex systems. Module structure not only is a crucial character of networks, but also is multi-scale. Therefore, many multi-scale module detection algorithms are proposed to resolve the problem. But a highly important issue for multi-scale methods is that of how to select crucial partitions among multi-scale network partitions so that these partitions can effectively help people to understand complex system. To solve the problem, we propose a novel partition-based hierarchical clustering to select significant network partitions. Experiments on selection of benchmark and real networks demonstrate that the new method for selecting significant partitions is very effectively.

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Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk

Cited by 8 publications

References 57 publications

A new multi-scale method to reveal hierarchical modular structures in biological networks

A new multi-scale method to reveal hierarchical modular structures in biological networks

Multi-scale View Reveals Easily Detectable Community in Complex Networks

Selection of Significant Community Structure Based on Network Partition-based Cluster

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