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Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach [article]

Gunnar Carlsson, Facundo Mémoli, Santiago Segarra
2021 arXiv   pre-print
We provide a complete taxonomic characterization of robust hierarchical clustering methods for directed networks following an axiomatic approach.  ...  Our main result is to leverage this generative model to give a precise characterization of all robust -- i.e., excisive, linear scale preserving, and stable -- hierarchical clustering methods for directed  ...  The authors would like to thank Alejandro Ribeiro for his valuable technical input and his thoughtful comments on an earlier version of this manuscript.  ... 
arXiv:2108.07247v1 fatcat:njnr3n6xlnc57dshgpt7v2j2bm

Core-Periphery in Networks: An Axiomatic Approach [article]

Chen Avin, Zvi Lotker, David Peleg, Yvonne Anne Pignolet, Itzik Turkel
2014 arXiv   pre-print
In particular, we show that a balance of powers between elite and periphery and an elite size that is sub-linear in the network size are universal properties of elites in social networks that satisfy our  ...  Herein, we present a novel axiom-based model for the forces governing the mutual influences between the elite and the periphery.  ...  In contrast, we propose an axiomatic approach to the questions at hand.  ... 
arXiv:1411.2242v1 fatcat:jwbuphtsavfijjookesldaeaju

Elites in social networks: An axiomatic approach to power balance and Price's square root law

Chen Avin, Zvi Lotker, David Peleg, Yvonne-Anne Pignolet, Itzik Turkel, Takashi Nishikawa
2018 PLoS ONE  
We accompany these findings with an empirical study on 32 real-world social networks, which provides evidence that balanced edge-based partitions which satisfying our axioms commonly exist.  ...  A common two-tier structure for social networks is based on partitioning society into two parts, referred to as the elite and the periphery, where the "elite" is the relatively small but well-connected  ...  Employing an axiomatic approach instead of providing an evolutionary model has two main advantages for studying social and complex networks.  ... 
doi:10.1371/journal.pone.0205820 fatcat:fywtr6wreva3lnsg2mofkxkxzu

Evaluation of remedial countermeasures using the analytic network process

M.A.B. Promentilla, T. Furuichi, K. Ishii, N. Tanikawa
2006 Waste Management  
We introduced a hierarchical network (hiernet) decision structure and applied the Analytic Network Process (ANP) supermatrix approach to measure the relative desirability of the remedial alternatives using  ...  The aim of this paper is to present an evaluation method to aid decision makers in the prioritization and selection of appropriate countermeasures at the planning stage of site remediation.  ...  The model can be represented as a directed network (digraph).  ... 
doi:10.1016/j.wasman.2005.11.020 pmid:16412627 fatcat:hukj4sm6onc6zcwfm3xxrdjnbm

Defining and Evaluating Network Communities based on Ground-truth [article]

Jaewon Yang, Jure Leskovec
2012 arXiv   pre-print
For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities.  ...  We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground-truth.  ...  We adopt the following axiomatic approach.  ... 
arXiv:1205.6233v3 fatcat:ixmqtvuhxbhyjc5lcwlqyvtele

Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks [article]

Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
2014 arXiv   pre-print
Allowing clustering in a two-node network to proceed at the minimum of the two dissimilarities generates an alternative axiomatic construction. There is a unique clustering method in this case too.  ...  This paper considers networks where relationships between nodes are represented by directed dissimilarities.  ...  An alternative approach for clustering nodes in graphs is the idea of spectral clustering [21] - [24] .  ... 
arXiv:1301.7724v2 fatcat:mfyoakowqvhs7g2kiru4kwjygy

Graph Metrics for Network Robustness—A Survey

Milena Oehlers, Benjamin Fabian
2021 Mathematics  
In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks.  ...  Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network  ...  axiomatic approach, only based on information contained in the Node Degree Distribution D(k) = P(k) • v.  ... 
doi:10.3390/math9080895 fatcat:c4n6duzywnenffrm6dxikojnwa

Complex networks in advanced manufacturing systems

Yongfeng Li, Fei Tao, Ying Cheng, Xianzhi Zhang, A.Y.C. Nee
2017 Journal of manufacturing systems  
It has provided an alternative approach to analyze AMSs. Many recent studies have focused on the theory of complex networks to describe and solve complicated manufacturing problems.  ...  Based on a great number of relevant publications, this paper presents an up-to-date literature review with the identified outstanding research issues, future trends and directions.  ...  Wang discusses the directed network system of supply chain networks for the product family [111] .  ... 
doi:10.1016/j.jmsy.2016.12.001 fatcat:jz5gmhlwpnc3dmhal3zbnuf7w4

A PRIORITIZATION METHOD USING HIERARCHICAL NETWORK MODEL FOR MULTI-CRITERIA EVALUATION OF REMEDIAL COUNTERMEASURES

Michael Angelo B. PROMENTILLA, Tohru FURUICHI, Kazuei ISHII, Noboru TANIKAWA
2006 Doboku Gakkai Ronbunshuu G  
This paper proposes a prioritization method using hierarchical network model to capture the complexity in the multi-criteria evaluation of remedial countermeasures.  ...  The proposed method which was built from the Analytic Network Process (ANP) uses the eigenvector approach to derive the local relative priorities and the overall relative priorities from the pairwise comparison  ...  Fig.2a is an example of a digraph representation of the four interacting clusters in a three-level hierarchical network.  ... 
doi:10.2208/jscejg.62.308 fatcat:2r7ushgrvvc33h7xcx6c4bnxpe

Defining and evaluating network communities based on ground-truth

Jaewon Yang, Jure Leskovec
2013 Knowledge and Information Systems  
For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities.  ...  We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground-truth.  ...  We adopt the following axiomatic approach.  ... 
doi:10.1007/s10115-013-0693-z fatcat:c3y3mrlnhfdjjeo2qsckif6glq

Defining and evaluating network communities based on ground-truth

Jaewon Yang, Jure Leskovec
2012 Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics - MDS '12  
For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities.  ...  We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground-truth.  ...  We adopt the following axiomatic approach.  ... 
doi:10.1145/2350190.2350193 fatcat:jufzrmkhe5cg7e4urtzgldi4kq

Defining and Evaluating Network Communities Based on Ground-Truth

Jaewon Yang, Jure Leskovec
2012 2012 IEEE 12th International Conference on Data Mining  
For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities.  ...  We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground-truth.  ...  We adopt the following axiomatic approach.  ... 
doi:10.1109/icdm.2012.138 dblp:conf/icdm/YangL12 fatcat:drtewcp5rrg5hm6xukvr4rddrm

Disrupting Terrorist Networks: A Dynamic Fitness Landscape Approach [chapter]

Philip Vos Fellman, Jonathan P. Clemens, Roxana Wright, Jonathan Vos Post, Matthew Dadmun
2015 Understanding Complex Systems  
In order to better represent the asymmetric nature of the 1 Replacement of the interactivity parameter, K, with an adjacency matrix is meant to let us generalize the NK approach in the directions presently  ...  The difficulty with this approach is that an important aspect of the dynamics of terrorist networks is that they are learning organizations (Hoffman, 1997; Tsvetovat and Carley, 2003) .  ... 
doi:10.1007/978-1-4939-1705-1_8 fatcat:keseftwoovcwtbbatcqsltf52a

Disrupting Terrorist Networks — A Dynamic Fitness Landscape Approach [chapter]

Philip V. Fellman, Jonathan P. Clemens, Roxana Wright, Jonathan Vos Post, Matthew Dadmun
2012 Unifying Themes in Complex Systems VII  
In order to better represent the asymmetric nature of the 1 Replacement of the interactivity parameter, K, with an adjacency matrix is meant to let us generalize the NK approach in the directions presently  ...  The difficulty with this approach is that an important aspect of the dynamics of terrorist networks is that they are learning organizations (Hoffman, 1997; Tsvetovat and Carley, 2003) .  ... 
doi:10.1007/978-3-642-18003-3_9 fatcat:cu7jwc3yfrgkfgzrdbcrow2mru

Relating Bisimulations with Attractors in Boolean Network Models [chapter]

Daniel Figueiredo
2016 Lecture Notes in Computer Science  
When studying a biological regulatory network, it is usual to use boolean network models. In these models, boolean variables represent the behavior of each component of the biological system.  ...  We also discuss the differences between using this approach and other well-known methodologies to study this kind of systems and we illustrate it with some examples.  ...  In [3] , this system is studied with a asynchronous boolean network and the attractors of the resulting network are found and it is studied the robustness of this model.  ... 
doi:10.1007/978-3-319-38827-4_2 fatcat:b6nrbrh6jffwxmzwepznolvznu
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