Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Verbrugge, D. Colle, P. Demeester, R. Huelsermann, M. Jaeger (2005)
General availability model for multilayer transport networksDRCN 2005). Proceedings.5th International Workshop on Design of Reliable Communication Networks, 2005.
B. Kröse (1995)
Learning from delayed rewardsRobotics Auton. Syst., 15
(2015)
Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems
Lei Tang, Xufei Wang, Huan Liu (2009)
Uncoverning Groups via Heterogeneous Interaction Analysis2009 Ninth IEEE International Conference on Data Mining
B. Karrer, M. Newman (2010)
Stochastic blockmodels and community structure in networksPhysical review. E, Statistical, nonlinear, and soft matter physics, 83 1 Pt 2
Haiying Wang, Huiru Zheng, Jianxin Wang, Chaoyang Wang, Fang-Xiang Wu (2016)
Integrating Omics Data With a Multiplex Network-Based Approach for the Identification of Cancer SubtypesIEEE Transactions on NanoBioscience, 15
Jaewon Yang, J. Leskovec (2013)
Overlapping community detection at scale: a nonnegative matrix factorization approachProceedings of the sixth ACM international conference on Web search and data mining
M. Barigozzi, G. Fagiolo, G. Mangioni (2010)
Identifying the Community Structure of the International-Trade Multi NetworkArXiv, abs/1009.1731
D. Bassett, O. Sporns (2017)
Network neuroscienceNature Neuroscience, 20
J. Leskovec, C. Faloutsos (2006)
Sampling from large graphs
Pascal Pons, Matthieu Latapy (2006)
Computing communities in large networks using random walksJournal of Graph Algorithms and Applications, 10
Michael Szell, R. Lambiotte, S. Thurner (2010)
Multirelational organization of large-scale social networks in an online worldProceedings of the National Academy of Sciences, 107
M. Domenico, Albert Solé-Ribalta, S. Gómez, A. Arenas (2013)
Navigability of interconnected networks under random failuresProceedings of the National Academy of Sciences, 111
M. Girvan, M. Newman (2001)
Community structure in social and biological networksProceedings of the National Academy of Sciences of the United States of America, 99
M. McPherson, L. Smith-Lovin, J. Cook (2001)
Birds of a Feather: Homophily in Social NetworksReview of Sociology, 27
M. Domenico, V. Nicosia, A. Arenas, V. Latora (2014)
Structural reducibility of multilayer networksNature Communications, 6
Ricky Laishram, Jeremy Wendt, S. Soundarajan (2019)
Crawling the Community Structure of Multiplex Networks
J. Scarlett, Ilija Bogunovic, V. Cevher (2019)
Overlapping Multi-Bandit Best Arm Identification2019 IEEE International Symposium on Information Theory (ISIT)
Haiying Wang, Huiru Zheng, Jianxin Wang, Chaoyang Wang, Fang-Xiang Wu (2016)
Integrating Omic Data with a Multiplex Network-based Approach for the Identification of Cancer Subtypes.IEEE transactions on nanobioscience, 15 4
International Crisis Group
ArrayTerrorism in Indonesia: Noordin’s Networks. Retrieved from https://www.crisisgroup.org/asia/south-east-asia/indonesia/terrorism-indonesia-noordin-s-networks.
Arun Maiya, T. Berger-Wolf (2010)
Online Sampling of High Centrality Individuals in Social Networks
Sean Everton (2012)
Disrupting Dark Networks
V. Vieira, C. Xavier, N. Ebecken, Alexandre Evsukoff (2014)
Modularity Based Hierarchical Community Detection in Networks
M. Newman (2006)
Modularity and community structure in networks.Proceedings of the National Academy of Sciences of the United States of America, 103 23
Wook-Shin Han, D. Srivastava, Ge Yu, Hwanjo Yu, Zi Huang (2010)
Advances in Web Technologies and Applications, Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010, Busan, Korea, 6-8 April 2010
V. Blondel, Jean-Loup Guillaume, R. Lambiotte, E. Lefebvre (2008)
Fast unfolding of communities in large networksJournal of Statistical Mechanics: Theory and Experiment, 2008
Chris Stark, B. Breitkreutz, T. Reguly, Lorrie Boucher, A. Breitkreutz, M. Tyers (2005)
BioGRID: a general repository for interaction datasetsNucleic Acids Research, 34
L. Danon, A. Díaz-Guilera, Jordi Duch, A. Arenas (2005)
Comparing community structure identificationJournal of Statistical Mechanics: Theory and Experiment, 2005
P. Pons, Matthieu Latapy (2004)
Computing Communities in Large Networks Using Random Walks
Christian Hübler, H. Kriegel, K. Borgwardt, Zoubin Ghahramani (2008)
Metropolis Algorithms for Representative Subgraph Sampling2008 Eighth IEEE International Conference on Data Mining
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, L. Akoglu, Danai Koutra, C. Faloutsos, Lei Li (2012)
RolX: structural role extraction & mining in large graphs
A. Condon, R. Karp (1999)
Algorithms for graph partitioning on the planted partition model
M. Jalili, Yasin Orouskhani, Milad Asgari, N. Alipourfard, M. Perc (2017)
Link prediction in multiplex online social networksRoyal Society Open Science, 4
Alessio Cardillo, M. Zanin, J. Gómez-Gardeñes, M. Romance, A. Amo, S. Boccaletti (2012)
Modeling the multi-layer nature of the European Air Transport Network: Resilience and passengers re-scheduling under random failuresThe European Physical Journal Special Topics, 215
Ludvig Bohlin, Daniel Edler, Andrea Lancichinetti, Martin Rosvall (2014)
Community detection and visualization of networks with the map equation frameworkMeasuring Scholarly Impact: Methods and Practice
Ludmila I. Kuncheva, Stefan Todorov Hadjitodorov (2004)
Using diversity in cluster ensemblesProceedings of the IEEE International Conference on Systems
Wook-Shin Han, Divesh Srivastava, Ge Yu, Hwanjo Yu, Zi Helen Huang (Eds (2010)
Advances in web technologies and applicationsIn Proceedings of the 12th Asia-Pacific Web Conference. IEEE Computer Society. Retrieved from https://ieeexplore.ieee.org/xpl/conhome/5473891/proceeding.
Andrea Lancichinetti, S. Fortunato (2011)
Limits of modularity maximization in community detectionPhysical review. E, Statistical, nonlinear, and soft matter physics, 84 6 Pt 2
Kyu-Min Lee, Byungjoon Min, K. Goh (2015)
Towards real-world complexity: an introduction to multiplex networksThe European Physical Journal B, 88
Jaewon Yang, Julian McAuley, J. Leskovec (2013)
Community Detection in Networks with Node Attributes2013 IEEE 13th International Conference on Data Mining
Michel Tokic, G. Palm (2011)
Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax
Andrea Lancichinetti, S. Fortunato, J. Kertész (2008)
Detecting the overlapping and hierarchical community structure in complex networksNew Journal of Physics, 11
S. Brin, Lawrence Page (1998)
The Anatomy of a Large-Scale Hypertextual Web Search EngineComput. Networks, 30
Bo Tian, Can Zhao, Feiyang Gu, Zengyou He (2017)
A two-step framework for inferring direct protein-protein interaction network from AP-MS dataBMC Systems Biology, 11
Martin Schaefer, Jean-Fred Fontaine, A. Vinayagam, P. Porras, E. Wanker, Miguel Andrade (2012)
HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality ScoresPLoS ONE, 7
Konstantin Avrachenkov, P. Basu, G. Neglia, Bruno Ribeiro, D. Towsley (2014)
Pay few, influence most: Online myopic network covering2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Michael Ovelgönne, A. Geyer-Schulz (2012)
An ensemble learning strategy for graph clustering
Desislava Hristova, A. Noulas, Chloë Brown, Mirco Musolesi, C. Mascolo (2015)
A multilayer approach to multiplexity and link prediction in online geo-social networksEpj Data Science, 5
R. Gallotti, M. Barthelemy (2015)
The multilayer temporal network of public transport in Great BritainScientific Data, 2
(2005)
International Conference on Systems , Man and Cybernetics Using Diversity in Cluster Ensembles
Jeremy Wendt, Randy Wells, R. Field, S. Soundarajan (2016)
On data collection, graph construction, and sampling in Twitter2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Mikko Kivelä, A. Arenas, M. Barthelemy, J. Gleeson, Y. Moreno, M. Porter (2013)
Multilayer networksJ. Complex Networks, 2
E. Omodei, M. Domenico, A. Arenas (2015)
Characterizing interactions in online social networks during exceptional eventsArXiv, abs/1506.09115
Ricky Laishram, Katchaguy Areekijseree, Sucheta Soundarajan (2017)
Predicted max degree sampling: Sampling in directed networks to maximize node coverage through crawlingProceedings of the IEEE Conference on Computer Communications Workshops
M. Berlingerio, Fabio Pinelli, Francesco Calabrese (2013)
ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkSData Mining and Knowledge Discovery, 27
Ricky Laishram, K. Areekijseree, S. Soundarajan
2016 Ieee International Conference on Big Data (big Data) Predicted Max Degree Sampling : Sampling in Directed Networks to Maximize Node Coverage through Crawling
Ludvig Bohlin, Daniel Edler, Andrea Lancichinetti, Martin Rosval (2014)
Measuring Scholarly Impact
Ana Fred, Anil Jain (2003)
Robust data clustering2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2
Tiago Peixoto (2013)
Hierarchical block structures and high-resolution model selection in large networksArXiv, abs/1310.4377
Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher (2019)
Overlapping multi-bandit best arm identificationIn Proceedings of the IEEE International Symposium on Information Theory. Retrieved from https://infoscience.epfl.ch/record/265112/files/MultiBandit_FULL.pdf.
N. Cesa-Bianchi, P. Fischer (1998)
Finite-Time Regret Bounds for the Multiarmed Bandit Problem
L. Verbrugge (1979)
Multiplexity in Adult FriendshipsSocial Forces, 57
Rajendra Akerkar (Ed (2011)
In Proceedings of the International Conference on Web Intelligence, Mining and SemanticsACM.
A. Clauset, M. Newman, Cristopher Moore (2004)
Finding community structure in very large networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 70 6 Pt 2
S. Abiteboul, M. Preda, G. Cobena (2003)
Adaptive on-line page importance computation
Aaron McDaid, Derek Greene, N. Hurley (2011)
Normalized Mutual Information to evaluate overlapping community finding algorithmsArXiv, abs/1110.2515
K. Areekijseree, Ricky Laishram, S. Soundarajan (2018)
Guidelines for Online Network Crawling: A Study of Data Collection Approaches and Network PropertiesProceedings of the 10th ACM Conference on Web Science
Andrea Lancichinetti, F. Radicchi, J. Ramasco, S. Fortunato (2010)
Finding Statistically Significant Communities in NetworksPLoS ONE, 6
API Rate Limits -Twitter Developers
P. Mucha, Thomas Richardson, K. Macon, M. Porter, J. Onnela (2009)
Community Structure in Time-Dependent, Multiscale, and Multiplex NetworksScience, 328
M. Katehakis, A. Veinott (1987)
The Multi-Armed Bandit Problem: Decomposition and ComputationMath. Oper. Res., 12
Aditya Grover, J. Leskovec (2016)
node2vec: Scalable Feature Learning for NetworksProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Mihaela Sardiu, M. Washburn (2011)
Building Protein-Protein Interaction Networks with Proteomics and Informatics Tools*The Journal of Biological Chemistry, 286
Mark Newman, Mark Newman, Michelle Girvan, Michelle Girvan (2003)
Finding and evaluating community structure in networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 69 2 Pt 2
Pan Zhang (2015)
Evaluating accuracy of community detection using the relative normalized mutual informationJournal of Statistical Mechanics: Theory and Experiment, 2015
S. Wasserman, Katherine Faust (1994)
Social Network Analysis: Methods and Applications
Mark E. J. Newman (2006)
Modularity and community structure in networksProceedings of the National Academy of Sciences, 103
M. Rosvall, Carl Bergstrom (2007)
Maps of random walks on complex networks reveal community structureProceedings of the National Academy of Sciences, 105
P. Auer, N. Cesa-Bianchi, P. Fischer (2002)
Finite-time Analysis of the Multiarmed Bandit ProblemMachine Learning, 47
P. Jaccard (1912)
THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1New Phytologist, 11
M. Gosak, R. Markovič, J. Dolenšek, Marjan Rupnik, M. Marhl, A. Stožer, M. Perc (2017)
Network science of biological systems at different scales: A review.Physics of life reviews, 24
Arun Maiya, T. Berger-Wolf (2010)
Sampling community structure
In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Jul 20, 2021
Keywords: Multplex networks
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.