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Problem Domains in Complex Networks

Mini Singh Ahuja
2016 IOSR Journal of Computer Engineering  
Complex networks are special graphs with non trivial topological properties-features that do not occur in simple networks such as lattices or random graphs.  ...  In this paper most important research domains related to complex networks are reviewed such as Community detection, Influence Maximization, network sampling etc.  ...  The research is going on this side of complex network too [2] . One can study how complex network properties evolve when the sample grows during the measurement.  ... 
doi:10.9790/0661-1805026568 fatcat:nxcrszgskjbkfmgzel4ml5a4sq

Exploring Complex Networks with Failure-Prone Agents [chapter]

Arles Rodríguez, Jonatan Gómez, Ada Diaconescu
2017 Lecture Notes in Computer Science  
Two exploration algorithms are studied: one random and one using a stigmergy model (that we propose).  ...  We study two motion algorithms -random and stigmergy. These are similar to the ones defined in [8]; as Lévy walks do not apply to non-directional spaces, like networks.  ...  The β parameter determines how regular the final network will be: β = 0 generates a regular network, β = 1 a random network, and in-between values a Small-World network [15] ( Fig. 1) .  ... 
doi:10.1007/978-3-319-62428-0_7 fatcat:buw5l5wyb5azromslazqcl5mbq

The Emergence Of Complex Network Patterns In Music Networks

Pedro Cano, Markus Koppenberger
2004 Zenodo  
Build models of networks that explain and help understand how they are created and how they evolve. 3.  ...  For example, how much of the network structure is due to content similarity and how much to the self-organization of the network.  ... 
doi:10.5281/zenodo.1417663 fatcat:6r7crfjshvezxlw76iruv3gor4

The art of community detection [article]

Natali Gulbahce, Sune Lehmann
2008 arXiv   pre-print
to the current viewpoint that networks in nature are highly complex and structured entities.  ...  Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks  ...  Hierarchy describes how the various structural elements are combined; how nodes a linked to form motifs, motifs are combined to form communities, and communities are joined into the entire network.  ... 
arXiv:0807.1833v1 fatcat:pyenxxtzhjcwbhvetfv67rvqny

Beyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networks [article]

Alexander P. Kartun-Giles, Ginestra Bianconi
2019 arXiv   pre-print
We are able to show significant differences between the topology of node neighbourhoods of real networks and the stochastic topology of null models of random simplicial complexes revealing local organisation  ...  We perform a large scale statistical analysis of the topology of node neighbourhoods of real networks by first constructing their clique complexes, and then computing their Betti numbers.  ...  In Sec. 2 we define networks, simplicial complexes and clique complexes, and discuss how the clique complexes can be extracted from a network dataset.  ... 
arXiv:1901.10978v1 fatcat:de34hkqnyve6xktplce3iowzjq

A Review of Graph and Network Complexity from an Algorithmic Information Perspective

Hector Zenil, Narsis Kiani, Jesper Tegnér
2018 Entropy  
We review the fragility of computable measures on the one hand and the invariant properties of algorithmic measures on the other demonstrating how current approaches to algorithmic complexity are misguided  ...  Information-theoretic-based measures have been useful in quantifying network complexity.  ...  The proposed refinement of the so-called principle of maximum entropy-or Maxent-based on algorithmic complexity demonstrates (formally and numerically) how not all ER networks are random [11] , and that  ... 
doi:10.3390/e20080551 pmid:33265640 fatcat:3gjsm5sbbrgk3fhj46sr6ilsui

The art of community detection

Natali Gulbahce, Sune Lehmann
2008 Bioessays  
to the current viewpoint that networks in nature are highly complex and structured entities.  ...  Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks  ...  Hierarchy describes how the various structural elements are combined; how nodes are linked to form motifs, motifs are combined to form communities and communities are joined into the entire network.  ... 
doi:10.1002/bies.20820 pmid:18800363 fatcat:d5ojqbppwvdzvd3kbvkfklt7ri

Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems

Yang Xu, Pengfei Liu, Xiang Li, Wei Ren
2014 The Scientific World Journal  
Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes  ...  were organized with different complex network topologies.  ...  Although the effects of complex networks are popular in large multiagent systems, not all distributed algorithms are tested under different complex network topologies in order to estimate how complex network  ... 
doi:10.1155/2014/407639 pmid:24982947 pmcid:PMC3996872 fatcat:dzhfzrnaxfcwxhifhmljel7xp4

Does Complex Learning Require Complex Connectivity? [chapter]

Carlos Rubén de la Mora-Basáñez, Alejandro Guerra-Hernández, Luc Steels
2006 Lecture Notes in Computer Science  
Small World and Scale Free network properties characterize many real complex phenomena.  ...  Experimental results in the context of Pragmatic Games, elucidate some required conditions to obtain the expected network properties when performing complex learning.  ...  These measures are used to determine how far of randomness, or near to order, a network is. Interesting networks were found in the middle between randomness and complete order.  ... 
doi:10.1007/11874850_61 fatcat:iscmhz2ylzf2lpt6aaq6paacjq

Impact of topology on the performance of communication networks

Pramode K. Verma, Ziping Hu
2011 2011 National Conference on Communications (NCC)  
Based on the available literature, classic network topologies are reviewed and analyzed.  ...  Furthermore, a new class of communication networks is introduced, and a topology design algorithm is proposed to improve network performance in terms of average path length. I.  ...  In this section, we are going to review three classic models to build complex networks, and analyze how complex networks formed on different construction principles perform, based on the APL. 1) Random  ... 
doi:10.1109/ncc.2011.5734760 fatcat:wd5o6odij5blhopzmoqntxppuy

Multiple-predators-based capture process on complex networks

Rajput Ramiz Sharafat, Cunlai Pu, Jie Li, Rongbin Chen, Zhongqi Xu
2017 Chinese Physics B  
Moreover, dense or homogeneous network structures are against the survival of the lamb.  ...  The predator/prey (capture) problem is a prototype of many network-related applications. We study the capture process on complex networks by considering multiple predators from multiple sources.  ...  Over the past decade, researchers have made great efforts to study the structural properties of complex networks [1, 2, 3] , the dynamical processes existing in complex networks [4] , and how they affect  ... 
doi:10.1088/1674-1056/26/3/038901 fatcat:nmuu4ffyhratdishscpsmdzeii

The Complexities of Global Systems History*

Andrea Jones-Rooy, Scott E. Page
2010 The Journal of The Historical Society  
P a g e The Complexities of Global Systems History * Only connect! That was the whole of her sermon.  ...  Random networks, on the other hand, are robust against targeted attacks, but they are more vulnerable than power law networks to random knockouts.  ...  The lack of randomness in contact structure affects how the disease spreads. To say that agents in a complex system are connected in some way should immediately bring to mind a network.  ... 
doi:10.1111/j.1540-5923.2010.00307.x fatcat:v6zz4rhworcuhi7fpmfpympwgu

Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks

Shudong Li, Lixiang Li, Yan Jia, Xinran Liu, Yixian Yang
2013 Mathematical Problems in Engineering  
Here, we probe how to identify the vulnerable nodes of complex networks in cascading failures, which was ignored before.  ...  Concerned with random attack (RA) and highest load attack (HL) on nodes, we model cascading dynamics of complex networks.  ...  Some indexes are proposed to measure how robust or vulnerable the complex networks are under different attacks [4] [5] [6] .  ... 
doi:10.1155/2013/938398 fatcat:bmk2ijrsvbe6lajxmsg5jyipuu

Abrupt efficiency collapse in real-world complex weighted networks: robustness decrease with link weights heterogeneity [article]

Michele Bellingeri, Daniele Bevacqua, Francesco Scotognella, Davide Cassi
2019 arXiv   pre-print
We find that highly heterogeneous networks experienced a faster efficiency decrease under nodes-links removal: i.e. the robustness of the real-world complex networks against both random than attack is  ...  Coli, Us Airports and Human brain real-world complex weighted networks.  ...  Boguna for sharing their real-world networks dataset.  ... 
arXiv:1901.06404v1 fatcat:t4zedqoh5rgx3jfrkk2f5inag4

Social Network Analysis Taxonomy Based on Graph Representation [article]

Andry Alamsyah, Budi Rahardjo, Kuspriyanto
2021 arXiv   pre-print
There are three approaches in the current social network analysis study: Graph Representation, Content Mining, and Semantic Analysis.  ...  This paper provides a taxonomy of social network analysis based on its graph representation.  ...  Random Walks study the walk-through network by following path at random, this study is to understand how information is spreading across the network.  ... 
arXiv:2102.08888v1 fatcat:fyd333tl2ncpva5lyyj53qanyu
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