A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.

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Two distributed semi-supervised algorithms for mining of complex networks using Giraph

10.1101/477463 ◽

2018 ◽

Author(s):

Erfan Farhangi Maleki ◽

Nasser Ghadiri ◽

Maryam Lotfi Shahreza ◽

Zeinab Maleki

Keyword(s):

Complex Networks ◽

Heterogeneous Networks ◽

Biological Networks ◽

Biological Network ◽

Drug Repositioning ◽

Label Propagation ◽

Large Graphs ◽

The Subject ◽

Scalable Methods

AbstractBackground and ObjectiveHeterogeneous complex networks are large graphs consisting of different types of nodes and edges. The process of mining and knowledge extraction from these networks is so complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required.MethodsIn this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network.ResultsWe compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.

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Traffic Dynamics on Complex Networks: A Survey

Mathematical Problems in Engineering ◽

10.1155/2012/732698 ◽

2012 ◽

Vol 2012 ◽

pp. 1-23 ◽

Cited By ~ 50

Author(s):

Shengyong Chen ◽

Wei Huang ◽

Carlo Cattani ◽

Giuseppe Altieri

Keyword(s):

Complex Networks ◽

Communication Networks ◽

Network Structure ◽

Traffic Dynamics ◽

Open Problems ◽

Transport Efficiency ◽

Traffic Routing ◽

Underlying Network ◽

Practical Implications

Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

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ccNetViz: a WebGL-based JavaScript library for visualization of large networks

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4527-4529

Author(s):

Ales Saska ◽

David Tichy ◽

Robert Moore ◽

Achilles Rasquinha ◽

Caner Akdas ◽

...

Keyword(s):

Systems Biology ◽

Complex Networks ◽

Open Source ◽

High Speed ◽

A Priori ◽

Supplementary Information ◽

Network Visualization ◽

Supplementary Data ◽

Web Based ◽

Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

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Modelling community structure and temporal spreading on complex networks

Computational Social Networks ◽

10.1186/s40649-021-00094-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Vesa Kuikka

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Detection ◽

Network Structure ◽

Network Connectivity ◽

Network Models ◽

Building Blocks ◽

Detection Algorithm ◽

Research Area ◽

Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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Social networks to biological networks: systems biology of Mycobacterium tuberculosis

Molecular BioSystems ◽

10.1039/c3mb25546h ◽

2013 ◽

Vol 9 (7) ◽

pp. 1584 ◽

Cited By ~ 4

Author(s):

Rohit Vashisht ◽

Anshu Bhardwaj ◽

OSDD Consortium ◽

Samir K. Brahmachari

Keyword(s):

Social Networks ◽

Mycobacterium Tuberculosis ◽

Systems Biology ◽

Biological Networks

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Improved influential nodes identification in complex networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202943 ◽

2021 ◽

pp. 1-9

Author(s):

Shi Dong ◽

Wengang Zhou

Keyword(s):

Theoretical Analysis ◽

Complex Networks ◽

Network Structure ◽

Information Entropy ◽

Network System ◽

Weighted Degree ◽

Identification Methods ◽

Hybrid Mechanism ◽

Influential Nodes ◽

Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽

10.1039/c7ra01557g ◽

2017 ◽

Vol 7 (37) ◽

pp. 23222-23233 ◽

Cited By ~ 6

Author(s):

Wei Liu ◽

Wen Zhu ◽

Bo Liao ◽

Haowen Chen ◽

Siqi Ren ◽

...

Keyword(s):

Systems Biology ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Network Structure ◽

Regulatory Network ◽

Regulatory Networks ◽

Central Problem ◽

Expression Data ◽

Redundancy Reduction ◽

Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

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Modern Technologies and Approaches for Decoding Non-Coding Rna-Mediated Biological Networks in Systems Biology and Their Applications

Computational Biology and Bioinformatics ◽

10.1201/b20026-8 ◽

2016 ◽

pp. 106-132

Author(s):

Devyani Samantarrai ◽

Mousumi Sahu ◽

Garima Singh ◽

Jyoti Roy ◽

Chandra Bhushan ◽

...

Keyword(s):

Systems Biology ◽

Biological Networks ◽

Non Coding Rna ◽

Modern Technologies

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Self Assembly of Model Polymers into Biological Random Networks

10.26434/chemrxiv.13143278 ◽

2020 ◽

Author(s):

Matthew Bailey ◽

Mark Wilson

Keyword(s):

Network Structure ◽

Biological Networks ◽

Self Assembly ◽

Short Range ◽

External Pressure ◽

Coarse Grained ◽

Random Networks ◽

Network Structures ◽

Polymer Model ◽

Microscope Images

<div>The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models.</div><div>Model polymers are developed, inspired by "worm-like'' curve models, that are shown to spontaneously self assemble</div><div>to form networks similar to those observed experimentally in biological systems.</div><div>These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales.</div><div>Metrics are developed (using a polygon-based framework)</div><div>which are useful for describing simulated networks and can also be applied to images of real networks.</div><div>These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order.</div><div>The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. </div><div>The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. </div><div>In addition, "pre-tangled'' network structures are introduced and shown to significantly influence the subsequent network structure.</div><div>The network structures obtained fit into a region of the network landscape effectively inaccessible to random</div><div>(entropically-driven) networks but which are occupied by experimentally-derived configurations.</div>

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Are all the word ranking methods the same?

International Journal of Modern Physics C ◽

10.1142/s0129183121501448 ◽

2021 ◽

pp. 2150144

Author(s):

Elham Najafi ◽

Alireza Valizadeh ◽

Amir H. Darooneh

Keyword(s):

Complex System ◽

Time Series ◽

Spatial Distribution ◽

Time Series Analysis ◽

Complex Networks ◽

Rank Correlation ◽

Ranking Methods ◽

Series Analysis ◽

Maximum Value

Text as a complex system is commonly studied by various methods, like complex networks or time series analysis, in order to discover its properties. One of the most important properties of each text is its keywords, which are extracted by word ranking methods. There are various methods to rank words of a text. Each method differently ranks words according to their frequency, spatial distribution or other word properties. Here, we aimed to explore how similar various word ranking methods are. For this purpose, we studied the rank correlation of some important word ranking methods for number of sample texts with different subjects and text sizes. We found that by increasing text size the correlation between ranking methods grows. It means that as the text size increases, the associated word ranks calculated by different ranking methods converge. Also, we found out that the rank correlations of word ranking methods approach their maximum value in the case of large enough texts.

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