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Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains.
The complexity of theorem-proving procedures. In Proceedings of the third annual ACM symposium on Theory of computing . ACM, 151–158
Stephen A Cook. 1971 · 1971
Earlier work this paper cites.
A survey of the reconstruction conjecture
Frank Harary. 1974 · 1974
Earlier work this paper cites.
Local structure in social networks
Paul W Holland and Samuel Leinhardt. 1976 · 1976
Earlier work this paper cites.
An algorithm for subgraph isomorphism
Julian R Ullmann. 1976 · 1976
Earlier work this paper cites.
Practical graph isomorphism
Brendan D McKay et al · 1981
Earlier work this paper cites.
Enumeration in graphs . Vol. 87
MA Bezem and Jan van Leeuwen. 1987 · 1987
Earlier work this paper cites.
A detailed analysis of random polling dynamic load balancing. In Parallel Architectures, Algorithms and Networks, 1994.(ISPAN), International Symposium on . IEEE, 382–389
Peter Sanders. 1994 · 1994
Earlier work this paper cites.
Social network analysis: Methods and applications . Vol. 8
Stanley Wasserman and Katherine Faust. 1994 · 1994
Earlier work this paper cites.
Color-coding
Noga Alon, Raphael Yuster, and Uri Zwick. 1995 · 1995
Earlier work this paper cites.
Finding and counting small induced subgraphs efficiently
Ton Kloks, Dieter Kratsch, and Haiko Müller. 2000 · 2000
Earlier work this paper cites.
Subgraph isomorphism in planar graphs and related problems
David Eppstein. 2002 · 2002
Earlier work this paper cites.
Network motifs: simple building blocks of complex networks
Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002 · 2002
Earlier work this paper cites.
gspan: Graph-based substructure pattern mining. In 2002 IEEE International Conference on Data Mining, 2002. Proceedings. IEEE, 721–724
Xifeng Yan and Jiawei Han. 2002 · 2002
Earlier work this paper cites.
Efficient mining of frequent subgraphs in the presence of isomorphism. In Third IEEE International Conference on Data Mining . IEEE, 549–552
Jun Huan, Wei Wang, and Jan Prins. 2003 · 2003
Earlier work this paper cites.
Structure and function of the feed-forward loop network motif
Shmoolik Mangan and Uri Alon. 2003 · 2003
Earlier work this paper cites.
nauty user’s guide (version 2.2)
Brendan D McKay. 2003 · 2003
Earlier work this paper cites.
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
Nadav Kashtan, Shalev Itzkovitz, Ron Milo, and Uri Alon. 2004 · 2004
Earlier work this paper cites.
Two’s company, three’s a crowd? Triads in cooperative-competitive networks
Ravindranath Madhavan, Devi R Gnyawali, and Jinyu He. 2004 · 2004
Earlier work this paper cites.
Superfamilies of evolved and designed networks
Ron Milo, Shalev Itzkovitz, Nadav Kashtan, Reuven Levitt, Shai Shen-Orr, Inbal Ayzenshtat, Michal Sheffer, and Uri Alon. 2004 · 2004
Earlier work this paper cites.
Motifs in brain networks
Olaf Sporns and Rolf Kötter. 2004 · 2004
Earlier work this paper cites.
Network motifs in integrated cellular networks of transcription–regulation and protein–protein interaction
Esti Yeger-Lotem, Shmuel Sattath, Nadav Kashtan, Shalev Itzkovitz, Ron Milo, Ron Y Pinter, Uri Alon, and Hanah Margalit. 2004 · 2004
Earlier work this paper cites.
Simple trophic modules for complex food webs
Jordi Bascompte and Carlos J Melián. 2005 · 2005
Earlier work this paper cites.
The gaston tool for frequent subgraph mining
Siegfried Nijssen and Joost N Kok. 2005 · 2005
Earlier work this paper cites.
Finding, counting and listing all triangles in large graphs, an experimental study. In International Workshop on Experimental and Efficient Algorithms . Springer, 606–609
Thomas Schank and Dorothea Wagner. 2005 · 2005
Earlier work this paper cites.
Frequency concepts and pattern detection for the analysis of motifs in networks
Falk Schreiber and Henning Schwöbbermeyer. 2005 · 2005
Earlier work this paper cites.
Network motifs in computational graphs: A case study in software architecture
Sergi Valverde and Ricard V Solé. 2005 · 2005
Earlier work this paper cites.
A parallel algorithm for extracting transcriptional regulatory network motifs. In Bioinformatics and Bioengineering, 2005. BIBE 2005. Fifth IEEE Symposium on . IEEE, 193–200
Tie Wang, Jeffrey W Touchman, Weiyi Zhang, Edward B Suh, and Guoliang Xue. 2005 · 2005
Earlier work this paper cites.
A faster algorithm for detecting network motifs. In WABI , Vol. 3692. Springer, 165–177
Sebastian Wernicke. 2005 · 2005
Earlier work this paper cites.
Structural comparison of metabolic networks in selected single cell organisms
Dongxiao Zhu and Zhaohui S Qin. 2005 · 2005
Earlier work this paper cites.
Psychological predispositions and network structure: The relationship between individual predispositions, structural holes and network closure
Yuval Kalish and Garry Robins. 2006 · 2006
Earlier work this paper cites.
Efficient estimation of graphlet frequency distributions in protein–protein interaction networks
N Pržulj, Derek G Corneil, and Igor Jurisica. 2006 · 2006
Earlier work this paper cites.
FANMOD: a tool for fast network motif detection
Sebastian Wernicke and Florian Rasche. 2006 · 2006
Earlier work this paper cites.
Network motif discovery using subgraph enumeration and symmetry-breaking. In Annual International Conference on Research in Computational Molecular Biology . Springer, 92–106
Joshua A Grochow and Manolis Kellis. 2007 · 2007
Earlier work this paper cites.
An optimal algorithm for counting network motifs
Royi Itzhack, Yelena Mogilevski, and Yoram Louzoun. 2007 · 2007
Earlier work this paper cites.
Biological network comparison using graphlet degree distribution
Nataša Pržulj. 2007 · 2007
Earlier work this paper cites.
Spontaneous emergence of modularity in cellular networks
Ricard V Solé and Sergi Valverde. 2007 · 2007
Earlier work this paper cites.
MapReduce: simplified data processing on large clusters
Jeffrey Dean and Sanjay Ghemawat. 2008 · 2008
Earlier work this paper cites.
Parallel data mining on graphics processors
Wenbin Fang, Ka Keung Lau, Mian Lu, Xiangye Xiao, Chi K Lam, Philip Yang Yang, Bingsheng He, Qiong Luo, Pedro V Sander, and Ke Yang. 2008 · 2008
Earlier work this paper cites.
Symmetry in complex networks
Ben D MacArthur, Rubén J Sánchez-García, and James W Anderson. 2008 · 2008
Earlier work this paper cites.
Assessing the exceptionality of network motifs
Franck Picard, J-J Daudin, Michel Koskas, Sophie Schbath, and Stephane Robin. 2008 · 2008
Earlier work this paper cites.
Looking at social capital through triad structures
Christina Prell and John Skvoretz. 2008 · 2008
Earlier work this paper cites.
Parallel network motif finding
Michael Schatz, Elliott Cooper-Balis, and Adam Bazinet. 2008 · 2008
Earlier work this paper cites.
Assessing the exceptionality of coloured motifs in networks
Sophie Schbath, Vincent Lacroix, and Marie-France Sagot. 2008 · 2008
Earlier work this paper cites.
Approximating the number of network motifs
Mira Gonen and Yuval Shavitt. 2009 · 2009
Earlier work this paper cites.
Kavosh: a new algorithm for finding network motifs
Zahra Razaghi Moghadam Kashani, Hayedeh Ahrabian, Elahe Elahi, Abbas Nowzari-Dalini, Elnaz Saberi Ansari, Sahar Asadi, Shahin Mohammadi, Falk Schreiber, and Ali Masoudi-Nejad. 2009 · 2009
Earlier work this paper cites.
Mapreduce-based pattern finding algorithm applied in motif detection for prescription compatibility network. In International Workshop on Advanced Parallel Processing Technologies . Springer, 341–355
Yang Liu, Xiaohong Jiang, Huajun Chen, Jun Ma, and Xiangyu Zhang. 2009 · 2009
Earlier work this paper cites.
MODA: an efficient algorithm for network motif discovery in biological networks
Saeed Omidi, Falk Schreiber, and Ali Masoudi-Nejad. 2009 · 2009
Earlier work this paper cites.
Strategies for network motifs discovery. In 2009 Fifth IEEE International Conference on e-Science . IEEE, 80–87
Pedro Ribeiro, Fernando Silva, and Marcus Kaiser. 2009 · 2009
Earlier work this paper cites.
Topological network alignment uncovers biological function and phylogeny
Oleksii Kuchaiev, Tijana Milenković, Vesna Memišević, Wayne Hayes, and Nataša Pržulj. 2010 · 2010
Earlier work this paper cites.
A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers). In Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures . ACM, 303–314
Charles E Leiserson and Tao B Schardl. 2010 · 2010
Earlier work this paper cites.
Efficient counting of network motifs. In Distributed Computing Systems Workshops (ICDCSW), 2010 IEEE 30th International Conference on . IEEE, 92–98
Dror Marcus and Yuval Shavitt. 2010 · 2010
Earlier work this paper cites.
Optimal network alignment with graphlet degree vectors
Tijana Milenković, Weng Leong Ng, Wayne Hayes, and Nataša Pržulj. 2010 · 2010
Earlier work this paper cites.
Efficient subgraph frequency estimation with g-tries
Pedro Ribeiro and Fernando Silva. 2010a · 2010
Earlier work this paper cites.
G-tries: an efficient data structure for discovering network motifs. In Proceedings of the 2010 ACM Symposium on Applied Computing . ACM, 1559–1566
Pedro Ribeiro and Fernando Silva. 2010b · 2010
Earlier work this paper cites.
Efficient parallel subgraph counting using g-tries. In Cluster Computing (CLUSTER), 2010 IEEE International Conference on . IEEE, 217–226
Pedro Ribeiro, Fernando Silva, and Luís Lopes. 2010a · 2010
Earlier work this paper cites.
Subgraph enumeration in large social contact networks using parallel color coding and streaming. In Parallel Processing (ICPP), 2010 39th International Conference on . IEEE, 594–603
Zhao Zhao, Maleq Khan, VS Anil Kumar, and Madhav V Marathe. 2010 · 2010
Earlier work this paper cites.
Information content of colored motifs in complex networks
Christoph Adami, Jifeng Qian, Matthew Rupp, and Arend Hintze. 2011 · 2011
Earlier work this paper cites.
Analyzing and modeling real-world phenomena with complex networks: a survey of applications
Luciano da Fontoura Costa, Osvaldo N Oliveira Jr, Gonzalo Travieso, Francisco Aparecido Rodrigues, Paulino Ribeiro Villas Boas, Lucas Antiqueira, Matheus Palhares Viana, and Luis Enrique Correa Rocha. 2011 · 2011
Earlier work this paper cites.
Evaluation of speedup of Monte Carlo calculations of two simple reactor physics problems coded for the GPU/CUDA environment
Aiping Ding, Tianyu Liu, Chao Liang, Wei Ji, Mark S Shephard, X George Xu, and Forrest B Brown. 2011 · 2011
Earlier work this paper cites.
Counting stars and other small subgraphs in sublinear-time
Mira Gonen, Dana Ron, and Yuval Shavitt. 2011 · 2011
Earlier work this paper cites.
Efficient parallel graph exploration on multi-core CPU and GPU. In Parallel Architectures and Compilation Techniques (PACT), 2011 International Conference on . IEEE, 78–88
Sungpack Hong, Tayo Oguntebi, and Kunle Olukotun. 2011 · 2011
Earlier work this paper cites.
NeMo: Fast count of network motifs
Michel Koskas, Gilles Grasseau, Etienne Birmelé, Sophie Schbath, and Stéphane Robin. 2011 · 2011
Earlier work this paper cites.
Integrative network alignment reveals large regions of global network similarity in yeast and human
Oleksii Kuchaiev and Nataša Pržulj. 2011 · 2011
Earlier work this paper cites.
Network science: Theory and applications
Ted G Lewis. 2011 · 2011
Earlier work this paper cites.
Comment on ’An optimal algorithm for counting networks motifs’
Sebastian Wernicke. 2011 · 2011
Earlier work this paper cites.
Guise: Uniform sampling of graphlets for large graph analysis. In Data Mining (ICDM), 2012 IEEE 12th International Conference on . IEEE, 91–100
Mansurul A Bhuiyan, Mahmudur Rahman, and M Al Hasan. 2012 · 2012
Earlier work this paper cites.
Comparison of co-authorship networks across scientific fields using motifs. In Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on . IEEE, 147–152
Sarvenaz Choobdar, Pedro Ribeiro, Sylwia Bugla, and Fernando Silva. 2012b · 2012
Earlier work this paper cites.
Motif Mining in Weighted Networks. In 2nd IEEE ICDM Workshop on Data Mining in Networks . IEEE, 210–217
Sarvenaz Choobdar, Pedro Ribeiro, and Fernando Silva. 2012a · 2012
Earlier work this paper cites.
Temporal networks
Petter Holme and Jari Saramäki. 2012 · 2012
Cited alongside, same era.
Netmode: Network motif detection without nauty
Xin Li, Douglas S Stones, Haidong Wang, Hualiang Deng, Xiaoguang Liu, and Gang Wang. 2012 · 2012
Cited alongside, same era.
Arboricity, h-index, and dynamic algorithms
Min Chih Lin, Francisco J Soulignac, and Jayme L Szwarcfiter. 2012 · 2012
Cited alongside, same era.
Rage–a rapid graphlet enumerator for large networks
Dror Marcus and Yuval Shavitt. 2012 · 2012
Cited alongside, same era.
Building blocks of biological networks: a review on major network motif discovery algorithms
Ali Masoudi-Nejad, Falk Schreiber, and Zahra Razaghi Moghadam Kashani. 2012 · 2012
Cited alongside, same era.
Accelerated motif detection using combinatorial techniques. In Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on . IEEE, 744–753
Graphlet decomposition: Framework, algorithms, and applications
Nesreen K Ahmed, Jennifer Neville, Ryan A Rossi, Nick G Duffield, and Theodore L Willke. 2017 · 2017
Later among the works it cites.
Flow Motifs in Soccer: What can passing behavior tell us?
Joris Bekkers and Shaunak Dabadghao. 2017 · 2017
Later among the works it cites.
Large-scale network motif learning with compression
Peter Bloem and Steven de Rooij. 2017 · 2017
Later among the works it cites.
E-CLoG: counting edge-centric local graphlets. In 2017 IEEE International Conference on Big Data (Big Data) . IEEE, 586–595
Vachik S Dave, Nesreen K Ahmed, and Mohammad Al Hasan. 2017 · 2017
Later among the works it cites.
Impact of Memory Space Optimization Technique on Fast Network Motif Search Algorithm
Himamshu and Sarika Jain. 2017 · 2017
Later among the works it cites.
Combinatorial algorithm for counting small induced graphs and orbits
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Luis AA Meira, Vinicius R Maximo, Alvaro L Fazenda, and Arlindo F da Conceicao. 2012 · 2012
Cited alongside, same era.
Scalable GPU graph traversal. In ACM SIGPLAN Notices , Vol. 47. ACM, 117–128
Duane Merrill, Michael Garland, and Andrew Grimshaw. 2012 · 2012
Cited alongside, same era.
Parallel discovery of network motifs
Pedro Ribeiro, Fernando Silva, and Luís Lopes. 2012 · 2012
Cited alongside, same era.
Symmetry compression method for discovering network motifs
Jianxin Wang, Yuannan Huang, Fang-Xiang Wu, and Yi Pan. 2012 · 2012
Cited alongside, same era.
Biological network motif detection: principles and practice
Elisabeth Wong, Brittany Baur, Saad Quader, and Chun-Hsi Huang. 2012 · 2012
Cited alongside, same era.
Sahad: Subgraph analysis in massive networks using hadoop. In Parallel & Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International . IEEE, 390–401
Zhao Zhao, Guanying Wang, Ali R Butt, Maleq Khan, VS Anil Kumar, and Madhav V Marathe. 2012 · 2012
Cited alongside, same era.
The index-based subgraph matching algorithm (ISMA): fast subgraph enumeration in large networks using optimized search trees
Sofie Demeyer, Tom Michoel, Jan Fostier, Pieter Audenaert, Mario Pickavet, and Piet Demeester. 2013 · 2013
Cited alongside, same era.
Tomaž Hočevar and Janez Demšar. 2017 · 2017
Later among the works it cites.
Efficiently counting all orbits of graphlets of any order in a graph using autogenerated equations
Ine Melckenbeeck, Pieter Audenaert, Didier Colle, and Mario Pickavet. 2017 · 2017
Later among the works it cites.
Parallel graph partitioning for complex networks
Henning Meyerhenke, Peter Sanders, and Christian Schulz. 2017 · 2017
Later among the works it cites.
Scalable subgraph counting using MapReduce. In Proceedings of the Symposium on Applied Computing . ACM, 1574–1581
Ahmad Naser-eddin and Pedro Ribeiro. 2017 · 2017
Later among the works it cites.
Efficient orbit-aware triad and quad census in directed and undirected graphs
Mark Ortmann and Ulrik Brandes. 2017 · 2017
Later among the works it cites.
Motifs in temporal networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining . ACM, 601–610
Ashwin Paranjape, Austin R Benson, and Jure Leskovec. 2017 · 2017
Later among the works it cites.
Escape: efficiently counting all 5-vertex subgraphs. In Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 1431–1440
Ali Pinar, C Seshadhri, and Vaidyanathan Vishal. 2017 · 2017
Later among the works it cites.
Estimation of graphlet statistics
Ryan A Rossi, Rong Zhou, and Nesreen K Ahmed. 2017 · 2017
Later among the works it cites.
Network motifs detection using random networks with prescribed subgraph frequencies. In International Workshop on Complex Networks . Springer, 17–29
Miguel EP Silva, Pedro Paredes, and Pedro Ribeiro. 2017 · 2017
Later among the works it cites.
Inferring Higher-Order Structure Statistics of Large Networks From Sampled Edges
Pinghui Wang, Yiyan Qi, John CS Lui, Don Towsley, Junzhou Zhao, and Jing Tao. 2017 · 2017
Later among the works it cites.
Triangle counting in large networks: a review
Mohammad Al Hasan and Vachik S Dave. 2018 · 2018
Later among the works it cites.
Sublinear-time algorithms for counting star subgraphs via edge sampling
Maryam Aliakbarpour, Amartya Shankha Biswas, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, and Anak Yodpinyanee. 2018 · 2018
Later among the works it cites.
Graphlet-orbit Transitions (GoT): A fingerprint for temporal network comparison
David Aparício, Pedro Ribeiro, and Fernando Silva. 2018 · 2018
Later among the works it cites.
Counting connected subgraphs with maximum-degree-aware sieving. In 29th International Symposium on Algorithms and Computation (ISAAC 2018) . Schloss Dagstuhl-Leibniz-Zentrum für Informatik
Andreas Bjorklund, Thore Husfeldt, Petteri Kaski, Mikko Kalle Henrik Koivisto, et al · 2018
Later among the works it cites.
Motif Counting Beyond Five Nodes
Marco Bressan, Flavio Chierichetti, Ravi Kumar, Stefano Leucci, and Alessandro Panconesi. 2018 · 2018
Later among the works it cites.
IncGraph: Incremental graphlet counting for topology optimisation
Robrecht Cannoodt, Joeri Ruyssinck, Jan Ramon, Katleen De Preter, and Yvan Saeys. 2018 · 2018
Later among the works it cites.
On the number of nonisomorphic subtrees of a tree
Éva Czabarka, László A Székely, and Stephan Wagner. 2018 · 2018
Later among the works it cites.
Listing k-cliques in sparse real-world graphs. In Proceedings of the 2018 World Wide Web Conference . International World Wide Web Conferences Steering Committee, 589–598
Maximilien Danisch, Oana Balalau, and Mauro Sozio. 2018 · 2018
Later among the works it cites.
From homogeneous to heterogeneous network alignment via colored graphlets
Shawn Gu, John Johnson, Fazle E Faisal, and Tijana Milenković. 2018 · 2018
Later among the works it cites.
The sketching complexity of graph and hypergraph counting. In 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) . IEEE, 556–567
John Kallaugher, Michael Kapralov, and Eric Price. 2018 · 2018
Later among the works it cites.
MTMO: an efficient network-centric algorithm for subtree counting and enumeration
Guanghui Li, Jiawei Luo, Zheng Xiao, and Cheng Liang. 2018 · 2018
Later among the works it cites.
An efficient network motif discovery approach for co-regulatory networks
Jiawei Luo, Lv Ding, Cheng Liang, and Nguyen Hoang Tu. 2018 · 2018
Later among the works it cites.
Fast analytical methods for finding significant labeled graph motifs
Giovanni Micale, Rosalba Giugno, Alfredo Ferro, Misael Mongiovì, Dennis Shasha, and Alfredo Pulvirenti. 2018 · 2018
Later among the works it cites.
gLabTrie: A Data Structure for Motif Discovery with Constraints
Misael Mongioví, Giovanni Micale, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti, and Dennis Shasha. 2018 · 2018
Later among the works it cites.
Motif discovery in biological network using expansion tree
Sabyasachi Patra and Anjali Mohapatra. 2018 · 2018
Later among the works it cites.
Butterfly Counting in Bipartite Networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2150–2159
Seyed-Vahid Sanei-Mehri, Ahmet Erdem Sariyuce, and Srikanta Tirthapura. 2018 · 2018
Later among the works it cites.
A new algorithm for counting independent motifs in probabilistic networks
Aisharjya Sarkar, Yuanfang Ren, Rasha Elhesha, and Tamer Kahveci. 2018 · 2018
Later among the works it cites.
MOSS-5: A fast method of approximating counts of 5-node graphlets in large graphs
Pinghui Wang, Junzhou Zhao, Xiangliang Zhang, Zhenguo Li, Jiefeng Cheng, John CS Lui, Don Towsley, Jing Tao, and Xiaohong Guan. 2018 · 2018
Later among the works it cites.
Software homology detection with software motifs based on function-call graph
Peng Wu, Junfeng Wang, and Bin Tian. 2018 · 2018
Later among the works it cites.
SSRW: A Scalable Algorithm for Estimating Graphlet Statistics Based on Random Walk. In International Conference on Database Systems for Advanced Applications . Springer, 272–288
Chen Yang, Min Lyu, Yongkun Li, Qianqian Zhao, and Yinlong Xu. 2018 · 2018
Later among the works it cites.
A Parallel Graphlet Decomposition Library for Large Graphs
Nesreen K. Ahmed. 2018 · 2019
Closest in time.
Network Motif Software
Uri Alon. 2018 · 2019
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Temporal network alignment via GoT-WAVE
David Aparício, Pedro Ribeiro, Tijana Milenković, and Fernando Silva. 2019 · 2019
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Efficiently counting complex multilayer temporal motifs in large-scale networks
Hanjo D Boekhout, Walter A Kosters, and Frank W Takes. 2019 · 2019
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Motif Counting Beyond Five Nodes
Marco Bressan. 2018 · 2019
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Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networks
Raphaël Charbey and Christophe Prieur. 2019 · 2019
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Mining Graphlet Counts in Online Social Networks
Xiaowei Chen. 2018 · 2019
Closest in time.
GraphLab PowerGraph implementation of 4-profile counting
Ehtna R. Elenberg. 2016 · 2019
Closest in time.
Network Motifs: A Survey. In International Conference on Advances in Computing and Data Sciences . Springer, 80–91
Deepali Jain and Ripon Patgiri. 2019 · 2019
Closest in time.
QuateXelero – Fast Motif Detection algorithm
Sahand Khakabimamaghani, Iman Sharafuddin, Norbert Dichter, Ina Koch, and Ali Masoudi-Nejad. 2018 · 2019
Closest in time.
Triadic Measures on Graphs: The Power of Wedge Sampling
Tamara Kolda, Ali Pinar, and C. Seshadhri. 2018 · 2019
Closest in time.
NetMODE SourceForge.net
Xin Li, Douglas S Stones, Haidong Wang, Hualiang Deng, Xiaoguang Liu, and Gang Wang. 2016 · 2019
Closest in time.
NeMo R Package (CRAN archive)
Dror Marcus and Yuval Shavitt. 2018 · 2019
Closest in time.
acc-Motif: Accelerated Motif Detection
Luís AA Meira, Vinícius R. Máximo, Ávaro L Fazenda, and Arlindo F da Conceição. 2018 · 2019
Closest in time.
Jesse - Tree-based algorithm to calculate graphlet densities of nodes in a graph using equations
Ine Melckenbeeck, Pieter Audenaert, Thomas Van Parys, Yves Van De Peer, Didier Colle, and Mario Pickavet. 2019a · 2019
Closest in time.
Optimising orbit counting of arbitrary order by equation selection
Ine Melckenbeeck, Pieter Audenaert, Thomas Van Parys, Yves Van De Peer, Didier Colle, and Mario Pickavet. 2019b · 2019
Closest in time.
Kavosh: a new algorithm for finding network motifs
Shahin Mohammadi. 2014 · 2019
Closest in time.
FaSE - Fast Subgraph Enumeration
Pedro Paredes and Pedro Ribeiro. 2018 · 2019
Closest in time.
ISMAGS - Enumerate all instances of a motif in a graph, making optimal use of the motif’s symmetries
Thomas V Parys and Ine Melckenbeeck. 2016 · 2019
Closest in time.
GRAFT: an approximate graphlet counting algorithm for large graph analysis
Mahmudur Rahman, Mansurul Bhuiyan, and Mahmuda Rahman. 2018a · 2019
Closest in time.
GUISE: Uniform Sampling of Graphlets for Large Graph Analysis
Mahmudur Rahman, Mansurul Bhuiyan, Mahmuda Rahman, and Mohammad Al Hasan. 2018b · 2019
Closest in time.
Finding Conserved Patterns in Multilayer Networks. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics . ACM, 97–102
Yuanfang Ren, Aisharjya Sarkar, Ahmet Ay, Alin Dobra, and Tamer Kahveci. 2019 · 2019
Closest in time.
gtrieScanner - Quick Discovery of Network Motifs
Pedro Ribeiro. 2018 · 2019
Closest in time.
GTScanner - Quick Discovery of Network Motifs
Pedro Ribeiro, David Aparício, Pedro Paredes, and Fernando Silva. 2017 · 2019
Closest in time.
RAGE - graphlet enumeration algorithm
Stéphane Robin, Etienne Birmelé, Michel Koskas, Gilles Grasseau, and Sophie Schbath. 2018 · 2019
Closest in time.
Ryan A Rossi, Nesreen K Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, and Eunyee Koh. 2019 · 2019
Closest in time.
Escape (Bitbucket)
C Seshadhri. 2017 · 2019
Closest in time.
GraphLab PowerGraph implementation of 4-profile counting
Saeed Shahrivari. 2016 · 2019
Closest in time.
Variational principle for scale-free network motifs
Clara Stegehuis, Remco van der Hofstad, and Johan SH van Leeuwaarden. 2019 · 2019
Closest in time.
MOSS-5: Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs
Pinghui Wang. 2018 · 2019
Closest in time.
FANMOD: a tool for fast network motif detection
Sebastian Wernicke. 2006 · 2019
Closest in time.
A Survey of Measures for Network Motifs
Feng Xia, Haoran Wei, Shuo Yu, Da Zhang, and Bo Xu. 2019 · 2019
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