| Submission declined on 26 January 2024 by
Johannes Maximilian (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
| Submission declined on 15 January 2024 by
Lewcm (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by
Lewcm 4 months ago.
| |
Comment: The sourcing in this draft makes no sense. This becomes immediately obvious in the lead section, e.g., footnote 4 is used as a reference for "EasyGraph is an open-source network analysis and network embedding". Even if we ignore that this sentence alone makes little sense, it wouldn't even need referencing because it's frankly too trivial. Also note that most sources cited don't even mention EasyGraph, i.e., they cannot and do not support any claims made here. I strongly suggest rewriting and resourcing this draft from scratch. Best regards, --
Johannes (
Talk) (
Contribs) (
Articles) 20:06, 26 January 2024 (UTC)
 | |
| Developer(s) | Min Gao, Zhen Li, Ruichen Li, Chenhao Cui, Xinyuan Chen, Bodian Ye, jiawei Li, Haoran Qin, Xinlei He, Yi Sun, Yuting Shao, Zihang Lin, Yang Chen, Qingyuan Gong |
|---|---|
| Initial release | 7 August 2023 [1] |
| Written in | Python, C++ |
| Operating system | Linux, Windows, macOS |
| Size | 3.2 MB |
| Available in | English |
| Type | Programming |
| License | BSD-3-Clause |
| Website |
easy-graph |
EasyGraph [2] [3] is an open-source network analysis and network embedding [4] software package. It is mainly written in Python and supports analysis for undirected networks and directed networks. EasyGraph supports various formats of network data and covers a series of important network analysis algorithms for node centrality analysis [5] , detecting community structure [6] [7] [8], structural hole spanner detection [9] [10] [11] [12], and graph representation [13] [14] [15] [16] [17]. Moreover, EasyGraph implements some key elements using C++ and introduces multiprocessing optimization [18]
to achieve better efficiency.
History
EasyGraph was developed by the DataNET group at Fudan University. Our goal is to build a cross-platform library which could be useful for interdisciplinary network analytics.
It's first version 1.0 has been launched in 2023.
Applications
EasyGraph has multiple notable applications including basic properties and operation of networks [19] [20]
, detection of structural hole spanners, network embedding [21] [22] [23] [24] [25], network construction [26] , and community detection [27] .
See also
- Graph (discrete mathematics)
- Graph drawing
- Graph theory
- Graph (data structure)
- Social network analysis software
- File formats
- Related software
- Gephi
- Cytoscape
- Graph-tool
- Graphviz
- Tulip (software)
- yEd
- AllegroGraph
- NetworkX
- NodeXL
- Pajek [28]
- NetMiner
References
- ^ https://github.com/easy-graph/Easy-Graph EasyGraph version 1.0 release date
- ^ Min Gao and Zheng Li and Ruichen Li and Chenhao Cui and Xinyuan Chen and Bodian Ye and Yupeng Li and Weiwei Gu and Qingyuan Gong and Xin Wang and Yang Chen (2023). "EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis". Patterns. 4 (10): 100839. doi: 10.1016/j.patter.2023.100839. PMC 10591136. PMID 37876903.
- ^ EasyGraph (2023-10-13). EasyGraph Tutorials. YouTube.
- ^ Grover, Aditya; Leskovec, Jure (2016). "Node2vec". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864. arXiv: 1607.00653. Bibcode: 2016arXiv160700653G. doi: 10.1145/2939672.2939754. ISBN 9781450342322. PMC 5108654. PMID 27853626.
- ^ Freeman, L.C. (1978). "Centrality in social networks conceptual clarification". Soc. Network. 1 (3): 215–239. doi: 10.1016/0378-8733(78)90021-7.
- ^ Newman, M.E.J. (2012). "Communities, modules and large-scale structure in networks". Nat. Phys. 8 (1): 25–31. Bibcode: 2012NatPh...8...25N. doi: 10.1038/nphys2162. S2CID 14973615.
-
^ Kong, Y.-X., Shi, G.-Y., Wu, R.-J., and Zhang, Y.-C. (2019). "k-core: Theories and applications". Phys. Rep. 832: 1–32.
Bibcode:
2019PhR...832....1K.
doi:
10.1016/j.physrep.2019.10.004.
S2CID
209065853.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q., Yu, Philip S. (2022). "A comprehensive survey on community detection with deep learning". IEEE Trans. Neural Netw. Learn. Syst. PP (4): 1–21.
arXiv:
2105.12584.
doi:
10.1109/TNNLS.2021.3137396.
PMID
35263257.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) - ^ Burt, R. (2004). "Structural holes and good ideas". American Journal of Sociology. 110 (2): 349–399. CiteSeerX 10.1.1.388.2251. doi: 10.1086/421787. S2CID 2152743.
-
^ Li, W., Xu, Z., Sun, Y., Gong, Q., Chen, Y., Ding, A.Y., Wang, X., and Hui, P. (2023).
"DeepPick: A Deep Learning Approach to Unveil Outstanding Users with Public Attainable Features". IEEE Trans. Knowl. Data Eng. 35: 291–306.
doi:
10.1109/TKDE.2021.3091503.
S2CID
238003420.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., Joyce, C., Shah, N., Sherman, K., Hecht, B., and Teevan, J. (2022). "The effects of remote work on collaboration among information workers". Nat. Hum. Behav. 6 (1): 43–54.
doi:
10.1038/s41562-021-01196-4.
PMID
34504299.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Li, P., Sun, X., Zhang, K., Zhang, J., and Kurths, J. (2016).
"Role of structural holes in containing spreading processes". Phys. Rev. E. 93 (3): 032312.
Bibcode:
2016PhRvE..93c2312L.
doi:
10.1103/PhysRevE.93.032312.
PMC
7217494.
PMID
27078371.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). "Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection". Proceedings of the Web Conference 2021. pp. 3168–3177.
doi:
10.1145/3442381.3449989.
ISBN
978-1-4503-8312-7.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Perozzi, B., Al-Rfou, R., and Skiena, S. (2014). "DeepWalk: Online learning of social representations". Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 701–710.
arXiv:
1403.6652.
doi:
10.1145/2623330.2623732.
ISBN
978-1-4503-2956-9.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Grover, A., and Leskovec, J. (2016). "Node2vec: Scalable Feature Learning for Networks". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864.
doi:
10.1145/2939672.2939754.
ISBN
978-1-4503-4232-2.
PMC
5108654.
PMID
27853626.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015). "LINE: Large-scale Information Network Embedding". Proceedings of the 24th International Conference on World Wide Web. pp. 1067–1077.
arXiv:
1503.03578.
doi:
10.1145/2736277.2741093.
ISBN
978-1-4503-3469-3.
S2CID
8399404.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Wang, D., Cui, P., and Zhu, W. (2016). "Structural Deep Network Embedding". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1225–1234.
doi:
10.1145/2939672.2939753.
ISBN
978-1-4503-4232-2.
S2CID
207238964.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Aziz, Z. A., Abdulqader, D. N., Sallow, A. B., & Omer, H. K. (2021). "Python parallel processing and multiprocessing: A review". Academic Journal of Nawroz University. 10 (3): 345–354.
doi:
10.25007/ajnu.v10n3a1145.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) - ^ Newman, M.E. (2018). Networks. Oxford University Press. doi: 10.1093/oso/9780198805090.001.0001. ISBN 978-0-19-880509-0.
-
^ Broido, A.D., and Clauset, A. (2019).
"Scale-free networks are rare". Nat. Commun. 10 (1): 1017–1010.
arXiv:
1801.03400.
Bibcode:
2019NatCo..10.1017B.
doi:
10.1038/s41467-019-08746-5.
PMC
6399239.
PMID
30833554.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). "Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection". Proceedings of the Web Conference 2021. pp. 3168–3177.
doi:
10.1145/3442381.3449989.
ISBN
978-1-4503-8312-7.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Perozzi, B., Al-Rfou, R., and Skiena, S. (2014). "DeepWalk: Online learning of social representations". Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 701–710.
arXiv:
1403.6652.
doi:
10.1145/2623330.2623732.
ISBN
978-1-4503-2956-9.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Grover, A., and Leskovec, J. (2016). "Node2vec: Scalable Feature Learning for Networks". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864.
doi:
10.1145/2939672.2939754.
ISBN
978-1-4503-4232-2.
PMC
5108654.
PMID
27853626.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015). "LINE: Large-scale Information Network Embedding". Proceedings of the 24th International Conference on World Wide Web. pp. 1067–1077.
arXiv:
1503.03578.
doi:
10.1145/2736277.2741093.
ISBN
978-1-4503-3469-3.
S2CID
8399404.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Wang, D., Cui, P., and Zhu, W. (2016). "Structural Deep Network Embedding". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1225–1234.
doi:
10.1145/2939672.2939753.
ISBN
978-1-4503-4232-2.
S2CID
207238964.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) - ^ Faust, K. (2021). "Open challenges for microbial network construction and analysis". The ISME Journal. 15 (11): 3111–3118. Bibcode: 2021ISMEJ..15.3111F. doi: 10.1038/s41396-021-01027-4. PMC 8528840. PMID 34108668.
- ^ Fortunato, S. (2010). "Community detection in graphs". Physics Reports. 486 (3–5): 75–174. arXiv: 0906.0612. Bibcode: 2010PhR...486...75F. doi: 10.1016/j.physrep.2009.11.002.
- ^ "Pajek / PajekXXL / Pajek3XL". mrvar.fdv.uni-lj.si. Retrieved 2019-12-09.
External links
Category:2000 software
Category:Network theory
Category:Free application software
Category:Network analysis software
Category:Free data analysis software
| Submission declined on 26 January 2024 by
Johannes Maximilian (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
| |
| Submission declined on 15 January 2024 by
Lewcm (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by
Lewcm 4 months ago.
| |
- Comment: The sourcing in this draft makes no sense. This becomes immediately obvious in the lead section, e.g., footnote 4 is used as a reference for "EasyGraph is an open-source network analysis and network embedding". Even if we ignore that this sentence alone makes little sense, it wouldn't even need referencing because it's frankly too trivial. Also note that most sources cited don't even mention EasyGraph, i.e., they cannot and do not support any claims made here. I strongly suggest rewriting and resourcing this draft from scratch. Best regards, --
Johannes (
Talk) (
Contribs) (
Articles) 20:06, 26 January 2024 (UTC)
|
| |
| Developer(s) | Min Gao, Zhen Li, Ruichen Li, Chenhao Cui, Xinyuan Chen, Bodian Ye, jiawei Li, Haoran Qin, Xinlei He, Yi Sun, Yuting Shao, Zihang Lin, Yang Chen, Qingyuan Gong |
|---|---|
| Initial release | 7 August 2023 [1] |
| Written in | Python, C++ |
| Operating system | Linux, Windows, macOS |
| Size | 3.2 MB |
| Available in | English |
| Type | Programming |
| License | BSD-3-Clause |
| Website |
easy-graph |
EasyGraph [2] [3] is an open-source network analysis and network embedding [4] software package. It is mainly written in Python and supports analysis for undirected networks and directed networks. EasyGraph supports various formats of network data and covers a series of important network analysis algorithms for node centrality analysis [5] , detecting community structure [6] [7] [8], structural hole spanner detection [9] [10] [11] [12], and graph representation [13] [14] [15] [16] [17]. Moreover, EasyGraph implements some key elements using C++ and introduces multiprocessing optimization [18]
to achieve better efficiency.
History
EasyGraph was developed by the DataNET group at Fudan University. Our goal is to build a cross-platform library which could be useful for interdisciplinary network analytics.
It's first version 1.0 has been launched in 2023.
Applications
EasyGraph has multiple notable applications including basic properties and operation of networks [19] [20]
, detection of structural hole spanners, network embedding [21] [22] [23] [24] [25], network construction [26] , and community detection [27] .
See also
- Graph (discrete mathematics)
- Graph drawing
- Graph theory
- Graph (data structure)
- Social network analysis software
- File formats
- Related software
- Gephi
- Cytoscape
- Graph-tool
- Graphviz
- Tulip (software)
- yEd
- AllegroGraph
- NetworkX
- NodeXL
- Pajek [28]
- NetMiner
References
- ^ https://github.com/easy-graph/Easy-Graph EasyGraph version 1.0 release date
- ^ Min Gao and Zheng Li and Ruichen Li and Chenhao Cui and Xinyuan Chen and Bodian Ye and Yupeng Li and Weiwei Gu and Qingyuan Gong and Xin Wang and Yang Chen (2023). "EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis". Patterns. 4 (10): 100839. doi: 10.1016/j.patter.2023.100839. PMC 10591136. PMID 37876903.
- ^ EasyGraph (2023-10-13). EasyGraph Tutorials. YouTube.
- ^ Grover, Aditya; Leskovec, Jure (2016). "Node2vec". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864. arXiv: 1607.00653. Bibcode: 2016arXiv160700653G. doi: 10.1145/2939672.2939754. ISBN 9781450342322. PMC 5108654. PMID 27853626.
- ^ Freeman, L.C. (1978). "Centrality in social networks conceptual clarification". Soc. Network. 1 (3): 215–239. doi: 10.1016/0378-8733(78)90021-7.
- ^ Newman, M.E.J. (2012). "Communities, modules and large-scale structure in networks". Nat. Phys. 8 (1): 25–31. Bibcode: 2012NatPh...8...25N. doi: 10.1038/nphys2162. S2CID 14973615.
-
^ Kong, Y.-X., Shi, G.-Y., Wu, R.-J., and Zhang, Y.-C. (2019). "k-core: Theories and applications". Phys. Rep. 832: 1–32.
Bibcode:
2019PhR...832....1K.
doi:
10.1016/j.physrep.2019.10.004.
S2CID
209065853.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q., Yu, Philip S. (2022). "A comprehensive survey on community detection with deep learning". IEEE Trans. Neural Netw. Learn. Syst. PP (4): 1–21.
arXiv:
2105.12584.
doi:
10.1109/TNNLS.2021.3137396.
PMID
35263257.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) - ^ Burt, R. (2004). "Structural holes and good ideas". American Journal of Sociology. 110 (2): 349–399. CiteSeerX 10.1.1.388.2251. doi: 10.1086/421787. S2CID 2152743.
-
^ Li, W., Xu, Z., Sun, Y., Gong, Q., Chen, Y., Ding, A.Y., Wang, X., and Hui, P. (2023).
"DeepPick: A Deep Learning Approach to Unveil Outstanding Users with Public Attainable Features". IEEE Trans. Knowl. Data Eng. 35: 291–306.
doi:
10.1109/TKDE.2021.3091503.
S2CID
238003420.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., Joyce, C., Shah, N., Sherman, K., Hecht, B., and Teevan, J. (2022). "The effects of remote work on collaboration among information workers". Nat. Hum. Behav. 6 (1): 43–54.
doi:
10.1038/s41562-021-01196-4.
PMID
34504299.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Li, P., Sun, X., Zhang, K., Zhang, J., and Kurths, J. (2016).
"Role of structural holes in containing spreading processes". Phys. Rev. E. 93 (3): 032312.
Bibcode:
2016PhRvE..93c2312L.
doi:
10.1103/PhysRevE.93.032312.
PMC
7217494.
PMID
27078371.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). "Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection". Proceedings of the Web Conference 2021. pp. 3168–3177.
doi:
10.1145/3442381.3449989.
ISBN
978-1-4503-8312-7.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Perozzi, B., Al-Rfou, R., and Skiena, S. (2014). "DeepWalk: Online learning of social representations". Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 701–710.
arXiv:
1403.6652.
doi:
10.1145/2623330.2623732.
ISBN
978-1-4503-2956-9.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Grover, A., and Leskovec, J. (2016). "Node2vec: Scalable Feature Learning for Networks". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864.
doi:
10.1145/2939672.2939754.
ISBN
978-1-4503-4232-2.
PMC
5108654.
PMID
27853626.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015). "LINE: Large-scale Information Network Embedding". Proceedings of the 24th International Conference on World Wide Web. pp. 1067–1077.
arXiv:
1503.03578.
doi:
10.1145/2736277.2741093.
ISBN
978-1-4503-3469-3.
S2CID
8399404.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Wang, D., Cui, P., and Zhu, W. (2016). "Structural Deep Network Embedding". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1225–1234.
doi:
10.1145/2939672.2939753.
ISBN
978-1-4503-4232-2.
S2CID
207238964.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Aziz, Z. A., Abdulqader, D. N., Sallow, A. B., & Omer, H. K. (2021). "Python parallel processing and multiprocessing: A review". Academic Journal of Nawroz University. 10 (3): 345–354.
doi:
10.25007/ajnu.v10n3a1145.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) - ^ Newman, M.E. (2018). Networks. Oxford University Press. doi: 10.1093/oso/9780198805090.001.0001. ISBN 978-0-19-880509-0.
-
^ Broido, A.D., and Clauset, A. (2019).
"Scale-free networks are rare". Nat. Commun. 10 (1): 1017–1010.
arXiv:
1801.03400.
Bibcode:
2019NatCo..10.1017B.
doi:
10.1038/s41467-019-08746-5.
PMC
6399239.
PMID
30833554.
{{ cite journal}}: CS1 maint: multiple names: authors list ( link) -
^ Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). "Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection". Proceedings of the Web Conference 2021. pp. 3168–3177.
doi:
10.1145/3442381.3449989.
ISBN
978-1-4503-8312-7.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Perozzi, B., Al-Rfou, R., and Skiena, S. (2014). "DeepWalk: Online learning of social representations". Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 701–710.
arXiv:
1403.6652.
doi:
10.1145/2623330.2623732.
ISBN
978-1-4503-2956-9.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Grover, A., and Leskovec, J. (2016). "Node2vec: Scalable Feature Learning for Networks". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2016. pp. 855–864.
doi:
10.1145/2939672.2939754.
ISBN
978-1-4503-4232-2.
PMC
5108654.
PMID
27853626.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015). "LINE: Large-scale Information Network Embedding". Proceedings of the 24th International Conference on World Wide Web. pp. 1067–1077.
arXiv:
1503.03578.
doi:
10.1145/2736277.2741093.
ISBN
978-1-4503-3469-3.
S2CID
8399404.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) -
^ Wang, D., Cui, P., and Zhu, W. (2016). "Structural Deep Network Embedding". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1225–1234.
doi:
10.1145/2939672.2939753.
ISBN
978-1-4503-4232-2.
S2CID
207238964.
{{ cite book}}: CS1 maint: multiple names: authors list ( link) - ^ Faust, K. (2021). "Open challenges for microbial network construction and analysis". The ISME Journal. 15 (11): 3111–3118. Bibcode: 2021ISMEJ..15.3111F. doi: 10.1038/s41396-021-01027-4. PMC 8528840. PMID 34108668.
- ^ Fortunato, S. (2010). "Community detection in graphs". Physics Reports. 486 (3–5): 75–174. arXiv: 0906.0612. Bibcode: 2010PhR...486...75F. doi: 10.1016/j.physrep.2009.11.002.
- ^ "Pajek / PajekXXL / Pajek3XL". mrvar.fdv.uni-lj.si. Retrieved 2019-12-09.
External links
Category:2000 software
Category:Network theory
Category:Free application software
Category:Network analysis software
Category:Free data analysis software