A seed-expanding method based on random walks for community detection in networks with ambiguous community structures

Su, Yansen; Wang, Bangju; Zhang, Xingyi

doi:10.1038/srep41830

Cited by 33 publications

(13 citation statements)

References 33 publications

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“…First, modules derived from blocks in SBMs are not only defined as clusters of tight relationships, i.e. co-expression clusters, as obtained with other clustering approaches 3, 9, 14 . In an SBM, nodes from the same block are characterized by common connectivity characteristics, i.e., common interaction profiles with nodes from the same and from other blocks.…”

Section: Discussionmentioning

confidence: 99%

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

2019

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Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics. Methods: The key challenge we address here is investigating the capability of stochastic block models (SBMs) for representing and analyzing different types of biomolecular networks. Fitting them to SBMs both delivers modules of the networks and enables the derivation of edge confidence scores, and it has not yet been investigated for analyzing biomolecular networks. We apply SBM-based analysis independently to three correlation-based networks of breast cancer data originating from high-throughput measurements of different molecular layers: either transcriptomics, proteomics, or metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness. Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biologically and phenotypically relevant functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. We conclude that biomolecular networks can be appropriately represented and analyzed by fitting SBMs. As the SBM-derived edge confidence scores are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are considered, they could be used as additional, integrated features in network-based data comparisons.

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Section: Discussionmentioning

confidence: 99%

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

2019

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“…Walktrap [30] performs random walk to compute the structural similarity between vertices and between communities and then repeatedly merges a pair of communities under the guidance of the distance which is defined using the similarity to acquire the resulting community structure. RWA [31] calculates the probability of a vertex belonging to each community based on random walks and then expands each of communities by absorbing the vertex which has the largest probability to belong to it. Attractor [32] proposes the concept of distance dynamics to detect communities.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the aforementioned IsoFdp [28] identifies seed vertices by finding points which are vertices mapped into a low-dimensional manifold with density peaks and then assigns other vertices to the nearest seeds. RWA [31] uses the degree centrality to select seed vertices from the network; it takes local maximum degree vertices as seeds of communities and then expands each of communities by adding the vertex which is most likely to belong to it repeatedly. Shang et al [34] proposed a community-detection algorithm which uses the degree centrality index to select the core vertices, then expands communities to include neighbor vertices which have larger similarity with the core vertex, and finally integrates some communities based on the proposed modularity density increment.…”

Section: Related Workmentioning

confidence: 99%

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

et al. 2020

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The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

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“…PPC algorithm [39] considers the network as a single community initially and recursively partitions each community utilizing node similarities computed using random walks until further partitioning cannot acquire a better value of modularity. RWA [40] employs random walks to calculate the probability of a node belonging to a community, and each community is expanded by repeatedly attracting the node which is most likely to belong to that community to join. Besides this, Attractor [41] utilizes distance dynamics to explore communities from networks, node interactions might change the distances among nodes, and the distance change will make an impact on the interaction in reverse.…”

Section: Related Workmentioning

confidence: 99%

Neighbor Similarity Based Agglomerative Method for Community Detection in Networks

Cheng

Yang

et al. 2019

Complexity

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Community structures can reveal organizations and functional properties of complex networks; hence, detecting communities from networks is of great importance. With the surge of large networks in recent years, the efficiency of community detection is demanded critically. Therefore, many local methods have emerged. In this paper, we propose a node similarity based community detection method, which is also a local one consisted of two phases. In the first phase, we first take out the node with the largest degree from the network to take it as an exemplar of the first community and insert its most similar neighbor node into the community as well. Then, the one with the largest degree in the remainder nodes is selected; if its most similar neighbor has not been classified into any community yet, we create a new community for the selected node and its most similar neighbor. Otherwise, if its most similar neighbor has been classified into a certain community, we insert the selected node into the community to which its most similar neighbor belongs. This procedure is repeated until every node in the network is assigned to a community; at that time, we obtain a series of preliminary communities. However, some of them might be too small or too sparse; edges connecting to outside of them might go beyond the ones inside them. Keeping them as the final ones will lead to a low-quality community structure. Therefore, we merge some of them in an efficient approach in the second phase to improve the quality of the resulting community structure. To testify the performance of our proposed method, extensive experiments are performed on both some artificial networks and some real-world networks. The results show that the proposed method can detect high-quality community structures from networks steadily and efficiently and outperform the comparison algorithms significantly.

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A seed-expanding method based on random walks for community detection in networks with ambiguous community structures

Cited by 33 publications

References 33 publications

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Neighbor Similarity Based Agglomerative Method for Community Detection in Networks

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