An algorithm based on positive and negative links for community detection in signed networks

Su, Yansen; Wang, Bangju; Cheng, Fan; Zhang, Lei; Zhang, Xingyi; Pan, Linqiang

doi:10.1038/s41598-017-11463-y

Cited by 25 publications

(8 citation statements)

References 33 publications

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“…Aiming at improving the accuracy of the results and reducing the convergence time while searching optimal partitions, recent research is trying to add some pre-processing steps before community transforming to collect more structural information from the network. For example, the community detection method based on positive/negative connections [15] runs a random walking process in the network and performs statistical analysis on the random-walking sequence. Then, the relationships of the nodes are evaluated as positive/negative for further detection.…”

Section: Related Workmentioning

confidence: 99%

MINC-NRL: An Information-Based Approach for Community Detection

Chen¹,

Chuanpeng²,

Liu³

2022

Algorithms

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Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks.

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Section: Related Workmentioning

confidence: 99%

MINC-NRL: An Information-Based Approach for Community Detection

Chen¹,

Chuanpeng²,

Liu³

2022

Algorithms

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“…Community detection in signed networks is also related to our work. For example, [8,16,[29][30][31]45] aim to find the antagonistic communities in a signed network. These works mainly focus on exploring several groups of dense subgraphs and most of them don't have a clear structural definition of their community model, while our work aims to enumerate the clique structure in a signed network.…”

Section: Related Workmentioning

confidence: 99%

“…In this experiment, we evaluate our algorithms on synthetic datasets. We use the synthetic signed network generator, SRN, to generate the synthetic datasets with default settings [45,56]. We generate four synthetic signed networks SN1-4 (details in Table 3) in different sizes and evaluate the efficiency of MBCEnum * and MBCEnum on SN1-4 similarly as Exp-1.…”

Section: Performance Studiesmentioning

confidence: 99%

Efficient Maximal Balanced Clique Enumeration in Signed Networks

Zi¹,

Yuan

Lin

et al. 2020

Proceedings of the Web Conference 2020

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Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. In real world, however, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks, and study the maximal balanced clique enumeration problem which computes all the maximal balanced cliques in a given signed network. We show that the maximal balanced clique enumeration problem is NP-Hard. A straightforward solution for the maximal balanced clique enumeration problem is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. We conduct extensive experiments on large real and synthetic datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms.

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“…It has, however, led us to the conclusion that a residue that moves in counterphase with a group of other residues should not be considered as a part the group, that is, the sign of cross-correlation should not be ignored. As the community analysis for signed networks is still in development (51,52), we decided to avoid negative links in the protein network by focusing on a rigid body detection approach. For this purpose, we used Local Spatial Pattern (LSP) alignment, a method we developed earlier to discover similar spatial patterns shared by different protein kinases (53,54).…”

Section: Introductionmentioning

confidence: 99%

Calculation of centralities in protein kinase A

Kornev

Aoto

Taylor

2022

Preprint

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Topological analysis of amino acid networks is a common method that can help to understand the roles of individual residues. The most popular approach for network construction is to create a connection between residues if they interact. These interactions are usually weighted by absolute values of correlation coefficients or mutual information. Here we argue that connections in such networks have to reflect levels of cohesion within the protein instead of a simple fact of interaction between residues. If this is correct, an indiscriminate combination of correlation and anti-correlation, as well as the all-inclusive nature of the mutual information metrics, should be detrimental for the analysis. To test our hypothesis, we studied amino acid networks of the protein kinase A created by Local Spatial Pattern alignment, a method that can detect conserved patterns formed by Cα-Cβ vectors. Our results showed that, in comparison with the traditional methods, this approach is more efficient in detecting functionally important residues. Out of four studied centrality metrics, Closeness centrality was the least efficient measure of residue importance. Eigenvector centrality proved to be ineffective as the spectral gap values of the networks were very low due to the bilobal structure of the kinase. We recommend using joint graphs of Betweenness centrality and Degree centrality to visualize different aspects of amino acid roles.

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An algorithm based on positive and negative links for community detection in signed networks

Cited by 25 publications

References 33 publications

MINC-NRL: An Information-Based Approach for Community Detection

MINC-NRL: An Information-Based Approach for Community Detection

Efficient Maximal Balanced Clique Enumeration in Signed Networks

Calculation of centralities in protein kinase A

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