How do biological networks differ from social networks? (an experimental study)

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A large amount of graph patterns, as well as graph generator models that mimic such patterns have been proposed over the last years. However, a deep and recurring question still remains: “What is a good pattern?” The answer is related to finding a pattern or a tool able to help distinguishing between actual real-world and fake graphs. Here we explore the ability of ShatterPlots, a simple and powerful algorithm to tease out patterns of real graphs, helping us to spot fake/masked graphs. The idea is to force a graph to reach a critical (“Shattering”) point, randomly deleting edges, and study its properties at that point.

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An Experimental Study of Search in Global Social Networks

Science ◽

10.1126/science.1081058 ◽

2003 ◽

Vol 301 (5634) ◽

pp. 827-829 ◽

Cited By ~ 416

Author(s):

P. S. Dodds

Keyword(s):

Social Networks ◽

Experimental Study

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COMMUNITY DETECTION IN SOCIAL NETWORKS EMPLOYING COMPONENT INDEPENDENCY

Modern Physics Letters B ◽

10.1142/s0217984909020242 ◽

2009 ◽

Vol 23 (17) ◽

pp. 2089-2106 ◽

Cited By ~ 3

Author(s):

ZHONGMIN XIONG ◽

WEI WANG

Keyword(s):

Social Networks ◽

Community Structure ◽

Theoretical Analysis ◽

Community Detection ◽

Biological Networks ◽

Real Data ◽

Important Task ◽

Underlying Structure ◽

Data Sets ◽

Detection Algorithms

Many networks, including social and biological networks, are naturally divided into communities. Community detection is an important task when discovering the underlying structure in networks. GN algorithm is one of the most influential detection algorithms based on betweenness scores of edges, but it is computationally costly, as all betweenness scores need to be repeatedly computed once an edge is removed. This paper presents an algorithm which is also based on betweenness scores but more than one edge can be removed when all betweenness scores have been computed. This method is motivated by the following considerations: many components, divided from networks, are independent of each other in their recalculation of betweenness scores and their split into smaller components. It is shown that this method is fast and effective through theoretical analysis and experiments with several real data sets, which have acted as test beds in many related works. Moreover, the version of this method with the minor adjustments allows for the discovery of the communities surrounding a given node without having to compute the full community structure of a graph.

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eXamine: Visualizing annotated networks in Cytoscape

F1000Research ◽

10.12688/f1000research.14612.1 ◽

2018 ◽

Vol 7 ◽

pp. 519

Author(s):

Philipp Spohr ◽

Kasper Dinkla ◽

Gunnar W. Klau ◽

Mohammed El-Kebir

Keyword(s):

Social Networks ◽

Biological Networks ◽

App Store ◽

Gene Sets ◽

Set Membership ◽

Small Set ◽

Reproducible Analysis

eXamine is a Cytoscape app that displays set membership as contours on top of a node-link layout of a small graph. In addition to facilitating interpretation of enriched gene sets of small biological networks, eXamine can be used in other domains such as the visualization of communities in small social networks. eXamine was made available on the Cytoscape App Store in March 2014, has since registered more than 7,200 downloads, and has been highly rated by more than 25 users. In this paper, we present eXamine's new automation features that enable researchers to compose reproducible analysis workflows to generate visualizations of small, set-annotated graphs.

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NODE MIXING AND GROUP STRUCTURE OF COMPLEX SOFTWARE NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525914500222 ◽

2014 ◽

Vol 17 (07n08) ◽

pp. 1450022 ◽

Cited By ~ 10

Author(s):

LOVRO ŠUBELJ ◽

SLAVKO ŽITNIK ◽

NELI BLAGUS ◽

MARKO BAJEC

Keyword(s):

Social Networks ◽

Software Engineering ◽

Biological Networks ◽

Group Structure ◽

Software Systems ◽

Node Degree ◽

The Internet ◽

Intrinsic Properties ◽

Software Projects ◽

Practical Applications

Large software projects are among most sophisticated human-made systems consisting of a network of interdependent parts. Past studies of software systems from the perspective of complex networks have already led to notable discoveries with different applications. Nevertheless, our comprehension of the structure of software networks remains to be only partial. Here we investigate correlations or mixing between linked nodes and show that software networks reveal dichotomous node degree mixing similar to that recently observed in biological networks. We further show that software networks also reveal characteristic clustering profiles and mixing. Hence, node mixing in software networks significantly differs from that in, e.g., the Internet or social networks. We explain the observed mixing through the presence of groups of nodes with common linking pattern. More precisely, besides densely linked groups known as communities, software networks also consist of disconnected groups denoted modules, core/periphery structures and other. Moreover, groups coincide with the intrinsic properties of the underlying software projects, which promotes practical applications in software engineering.

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A Survey on Overlapping Communities in Large-Scale Social Networks

Modern Technologies for Big Data Classification and Clustering - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2805-0.ch008 ◽

2018 ◽

pp. 198-215

Author(s):

S Rao Chintalapudi ◽

H. M. Krishna Prasad M

Keyword(s):

Social Networks ◽

Community Detection ◽

Biological Networks ◽

Modern World ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Detection Algorithms ◽

Research Areas ◽

Network Generator ◽

Overlapping Community

Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and biological networks and so on. The most important property of social networks is community, collection of nodes with dense connections inside and sparse connections at outside. Community detection is similar to clustering analysis and has many applications in the real-time world such as recommendation systems, target marketing and so on. Community detection algorithms are broadly classified into two categories. One is disjoint community detection algorithms and the other is overlapping community detection algorithms. This chapter reviews overlapping community detection algorithms with their strengths and limitations. To evaluate these algorithms, a popular synthetic network generator, i.e., LFR benchmark generator and the new extended quality measures are discussed in detail.

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