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Assortativity in Chung Lu Random Graph Models

Stephen Mussmann, John Moore, Joseph J. Pfeiffer, Jennifer Neville
2014 Proceedings of the 8th Workshop on Social Network Mining and Analysis - SNAKDD'14  
As such, it is difficult to incorporate direct optimization of assortativity into edge-based generative models.  ...  of assortativity into model representations.  ...  explicitly incorporate patterns of assortativity into model representations.  ... 
doi:10.1145/2659480.2659495 dblp:conf/kdd/MussmannMPN14 fatcat:2ppgtpmgl5h43ff54t2t7rbwni

Block-Approximated Exponential Random Graphs [article]

Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie
2020 arXiv   pre-print
This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction.  ...  ., edge independent) distributions, while being able to meaningfully model both local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity  ...  European Union's Seventh Framework Programme (FP7/2007-2013) / ERC Grant Agreement no. 615517, from the Flemish Government under the "Onderzoeksprogramma Artificile Intelligentie (AI) Vlaanderen" programme, and  ... 
arXiv:2002.07076v2 fatcat:wphj32ghozeqlcljj2rrieafp4

Block-Approximated Exponential Random Graphs

Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie
2020 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)  
This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction.  ...  ., edge independent) distributions, while being able to meaningfully model local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity, etc  ...  Union's Seventh Framework Programme (FP7/2007-2013) / ERC Grant Agreement no. 615517, from the Flemish Government under the "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen" programme, and  ... 
doi:10.1109/dsaa49011.2020.00019 dblp:conf/dsaa/AdriaensMLB20 fatcat:tqfefe3hhbbsthnbenb5ijtsge

Assortative-Constrained Stochastic Block Models [article]

Daniel Gribel, Thibaut Vidal, Michel Gendreau
2020 arXiv   pre-print
In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that this characteristic can lead to undesirable outcomes for networks  ...  Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities.  ...  We recommend to further evaluate the impact of assortativity constraints on known phase transitions and thresholds.  ... 
arXiv:2004.11890v1 fatcat:6sgfttdfmreffksfdiiyajozba

Demographic and Structural Characteristics to Rationalize Link Formation in Online Social Networks [article]

Muhammad Qasim Pasta, Zohaib Jan, Faraz Zaidi, Celine Rozenblat
2013 arXiv   pre-print
A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks.  ...  In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model growth of a network.We use different publicly available Facebook datasets as benchmarks  ...  A growing network model [11] was proposed to incorporate the assortative mixing behavior in social networks.  ... 
arXiv:1311.3508v1 fatcat:iqdtfiwxgjc65oherh42o5wrtu

Beyond Assortativity: Proclivity Index for Attributed Networks (ProNe) [chapter]

Reihaneh Rabbany, Dhivya Eswaran, Artur W. Dubrawski, Christos Faloutsos
2017 Lecture Notes in Computer Science  
Furthermore, PRONE can be computed fast in time linear in the network size and is highly useful, with applications in data imputation, marketing, personalization and privacy protection.  ...  Our work bridges this gap by addressing the following questions: Given the network structure, (i) which attributes and (ii) which pairs of attributes show correlation?  ...  To incorporate the attribute correlations into this model, [19] an acceptreject sampling framework was used to filter the edges generated from the underlying model and selectively accept those that match  ... 
doi:10.1007/978-3-319-57454-7_18 fatcat:fnfsqtrj4rayxmam6co6inojtu

Edge direction and the structure of networks

J. G. Foster, D. V. Foster, P. Grassberger, M. Paczuski
2010 Proceedings of the National Academy of Sciences of the United States of America  
We define a set of four directed assortativity measures and assign statistical significance by comparison to randomized networks.  ...  Our measures (i) reveal patterns common to each class, (ii) separate networks that have been previously classified together, and (iii) expose limitations of several existing theoretical models.  ...  Davidsen, and Seung-Woo Son for their thoughtful reading of the manuscript, the reviewers for their useful comments, and Juyong Park for an insightful discussion.  ... 
doi:10.1073/pnas.0912671107 pmid:20505119 pmcid:PMC2890716 fatcat:3igl4lbzlzbwtowfhshfanxdbe

Combining network theory with corporate governance: Converging models for connected stakeholders

Roberto Moro Visconti
2019 Corporate Ownership and Control  
Even though both corporate governance and network science are well-grounded theories, their possible connections have been hardly investigated.  ...  Acknowledgements: The author wishes to thank the participants to the AIDEA Conference in Turin, Italy (September 12, 2019) and the Virtus Interpress Conference in Naples, Italy (October 4, 2019) for their  ...  In assortative networks, hubs tend to connect to other hubs and small-degree nodes to similar nodes.  ... 
doi:10.22495/cocv17i1art12 fatcat:nbcgx7ttknayrc4duqtm45t764

A Big Data Architecture for Log Data Storage and Analysis [chapter]

Swapneel Mehta, Prasanth Kothuri, Daniel Lanza Garcia
2018 Studies in Computational Intelligence  
We adopt machine learning models with an ensemble of approaches to filter and process the indicators within the data and aim to predict anomalies or outliers using feature vectors built from this log data  ...  Our system uses Flume agents to send notifications to a Hadoop Distributed File System for long-term storage and ElasticSearch and Kibana for short-term visualisation, effectively creating a data lake  ...  Flume offers reliability due to the arsenal of failover and recovery mechanisms incorporated into a tuneable set of options.  ... 
doi:10.1007/978-981-10-8797-4_22 fatcat:qmhhnfyarzc7bgutk63qtptwpm

Annotated hypergraphs: models and applications

Philip Chodrow, Andrew Mellor
2020 Applied Network Science  
Annotated hypergraphs form a highly general framework for incorporating metadata into polyadic graph models.  ...  We proceed to formulate several metrics and algorithms for the analysis of annotated hypergraphs. Several of these, such as assortativity and modularity, naturally generalize dyadic counterparts.  ...  Acknowledgments The idea for this research was born at the 2019 SIAM Workshop on Network Science, organized by Nina Fefferman and Peter Mucha.  ... 
doi:10.1007/s41109-020-0252-y fatcat:myr2z72rybam7hkj4kfpw7lx4e

A unified view of generative models for networks: models, methods, opportunities, and challenges [article]

Abigail Z. Jacobs, Aaron Clauset
2014 arXiv   pre-print
Research on probabilistic models of networks now spans a wide variety of fields, including physics, sociology, biology, statistics, and machine learning.  ...  Here, we describe a unified view of generative models for networks that draws together many of these disparate threads and highlights the fundamental similarities and differences that span these fields  ...  Acknowledgments This work was supported by the US AFOSR and DARPA grant number FA9550-12-1-0432 (AZJ, AC) and the NSF Graduate Research Fellowship award number DGE 1144083 (AZJ).  ... 
arXiv:1411.4070v1 fatcat:6z2nwwdxlnhb5cxflpprgosmza

Reciprocal versus Parasocial Relationships in Online Social Networks [article]

Neil Zhenqiang Gong, Wenchang Xu
2014 arXiv   pre-print
edge will turn into a reciprocal one are basic research problems.  ...  For instance, we find that reciprocal edges are more likely to connect users with similar degrees while parasocial edges are more likely to link ordinary users (e.g., users with low degrees) and popular  ...  [10] designed a scalable model that can match all specified degree distributions.  ... 
arXiv:1302.6309v4 fatcat:vmp2yhpsbvatja537xzmksbdgi

Reciprocal versus parasocial relationships in online social networks

Neil Zhenqiang Gong, Wenchang Xu
2014 Social Network Analysis and Mining  
edge will turn into a reciprocal one are basic research problems.  ...  For instance, we find that reciprocal edges are more likely to connect users with similar degrees while parasocial edges are more likely to link ordinary users (e.g., users with low degrees) and popular  ...  [10] designed a scalable model that can match all specified degree distributions.  ... 
doi:10.1007/s13278-014-0184-6 fatcat:3lpwlvlvdzhwrhhyixfjg3lhp4

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Taylor Anderson, Suzana Dragićević
2020 ISPRS International Journal of Geo-Information  
The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution  ...  The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway's Game of Life model and (2) Schelling's model of segregation.  ...  Acknowledgments: The authors are thankful to the Natural Sciences and Engineering Research Council (NSERC) of Canada programs and SFU-SSHRC small institutional grant for the support of this research study  ... 
doi:10.3390/ijgi9040270 fatcat:q6wxfkwqs5herpahxtwlc6imeu

A review of stochastic block models and extensions for graph clustering

Clement Lee, Darren J. Wilkinson
2019 Applied Network Science  
We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data.  ...  We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups  ...  Acknowledgements Not applicable Authors' contributions CL compiled the articles reviewed and wrote the manuscript. Both authors reviewed and approved the final manuscript.  ... 
doi:10.1007/s41109-019-0232-2 fatcat:xcr4fe2rpbcrnfvwgy7sxypj2m
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