|
[1] SCHADT E E. Molecular networks as sensors and drivers of common human diseases. Nature, 2009, 461(7261):
218 -223.
[2] BHAGAT S, CORMODE G, MUTHUKRISHNAN S. Node classification in social networks. AGGARWAL C C
(ed). Social Network Data Analytics. Boston, MA, USA: Springer,
2011: 115 -148.
[3] XIE Z, OUYANG Z Z, LIU Q, et al. A geometric
graph model for citation networks of exponentially growing scientific papers.
Physica A: Statistical Mechanics and its Applications, 2016, 456:
167 - 175.
[4] ECKLES D, ESFANDIARI H, MOSSEL E, et al.
Seeding with costly network information. Proceedings of the
2019 ACM Conference on Economics and Computation (EC'19), 2019, Jun 24 - 28, Phoenix, AZ, USA. New York, NY, USA: ACM, 2019: 421 -422.
[5] XIAO S X, WANG S P, DAI Y F, et al. Graph
neural networks in node classification: survey and evaluation.
Machine Vision and
Applications, 2022, 33(1): 1 -19.
[6] TANG J L, AGGARWAL C, LIU H. Node
classification in signed social networks. Proceedings of the 2016 SIAM
International Conference on Data Mining (SDM'16), 2016, May 5 -7, Miami, FL, USA. Philadelphia, PA, USA: SIAM, 2016: 54 -62.
[7] ZHAO H, LI Z W, YOU Z H, et al. Predicting
mirna-disease associations based on neighbor selection graph
attention networks. IEEE/ ACM Transactions on Computational Biology and Bioinformatics, 2023, 20(2): 1298 -1307.
[8] JIA N, TIAN X L, ZHANG Y, et al.
Semi-supervised node classification with discriminable squeeze
excitation graph convolutional networks. IEEE Access, 2020, 8:
148226 -148236.
[9] WU Z H, PAN S R, CHEN F W, et al. A
comprehensive survey on graph neural networks. IEEE Transactions on
Neural Networks and Learning Systems, 2020, 32(1): 4 -24.
[10] ZHOU J, CUI G Q, HU S D, et al. Graph neural
networks: A review of methods and applications. AI Open, 2020,
1: 57 -81.
[11] CRASWELL N, SZUMMER M. Random walks on the
click graph. Proceedings of the 30th Annual International ACM
SIGIR Conference on Research and Development in
Information Retrieval (SIGIR'07), 2007, Jul 23 - 27, Amsterdam, Netherlands. New York, NY, USA: ACM, 2007: 239 -246.
[12] RIBA P, FISCHER A, LLADÓS J, et al. Learning graph distances with message passing neural networks.
Proceedings of the 24th International Conference on Pattern
Recognition (ICPR'18), 2018, Aug 20 - 24, Beijing, China. Piscataway, NJ,
USA: IEEE, 2018: 2239 -2244.
[13] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk:
Online learning of social representations. Proceedings of
the 20th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining (KDD'14), 2014, Aug 24 - 27, New York, USA. New York, NY, USA: ACM, 2014: 701 -710.
[14] MIKOLOV T, SUTSKEVER I, CHEN K, et al.
Distributed representations of words and phrases and their
compositionality. Advances in Neural Information Processing Systems
26: Proceedings of the 27th International Conference
on Neural Information Processing Systems ( NIPS'13 ): Vol 2, 2013, Dec 5 -10, Lake Tahoe, NV, USA. Red Hook, NY, USA: Curran Associates, 2013: 3111 -3119.
[15] GROVER A, LESKOVEC J. node2vec: Scalable
feature learning for networks. Proceedings of the 22nd ACM SIGKDD
International
Conference on Knowledge Discovery and Data Mining
(KDD'16), 2016, Aug 13 -17, San Francisco, CA, USA. New
York, NY, USA: ACM, 2016: 855 -864.
[16] HOFF P D, RAFTERY A E, HANDCOCK M S. Latent
space approaches to social network analysis. Journal of
the American Statistical Association, 2002, 97(460): 1090 -1098.
[17] GILMER J, SCHOENHOLZ S S, RILEY P F, et al.
Message passing neural networks. SCHÜTT K T, STEFAN CHMIELA S, VON LILIENFELD O A, et al (eds). Machine Learning
Meets Quantum Physics. LNP 968. Berlin, Germany:
Springer, 2020: 199 -214.
[18] NIKOLENTZOS G, TIXIER A J P, VAZIRGIANNIS M.
Message passing attention networks for document
understanding. Proceedings of the 34th AAAI Conference on
Artificial Intelligence (AAAI'20), 2020, Feb 7 - 12, New York, NY, USA. Menlo Park, CA, USA: AAAI, 2020: 8544 -8551.
[19] KIPF T N, WELLING M. Semi-supervised
classification with graph convolutional networks. Proceedings of the
5th International Conference on Learning Representations (ICLR'17), 2017, Apr 24 -26, Toulon, France. 2017: 1 -14.
[20] HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs. Advances
in Neural Information Processing Systems 30: Proceedings of
the 31st International Conference on Neural Information
Processing Systems (NIPS'17), 2017, Dec 4 - 9, Long Beach, CA, USA. Red Hook, NY, USA: Curran Associates, 2017: 1025 -1035.
[21] VELICKOVI'C P, CUCURULL G, CASANOVA A, et al.
Graph attention networks. Proceedings of the 6th
International Conference
on Learning Representations (ICLR'18), 2018, Apr 30 - May 3, Vancouver, Canada. 2018: 1 -12.
[22] VASWANI A, SHAZEER N, PARMAR N, et al.
Attention is all you need. Advances in Neural Information
Processing Systems 30: Proceedings of the 31st International Conference
on Neural Information Processing Systems ( NIPS'17), 2017, Dec 4 - 9, Long Beach, CA, USA. Red Hook, NY, USA: Curran Associates, 2017: 6000 -6010.
[23] HE T T, ONG Y S, BAI L. Learning conjoint
attentions for graph neural nets. Advances in Neural Information
Processing Systems
34: Proceedings of the 35th International
Conference on Neural Information Processing Systems (NIPS'21), 2021,
Dec 6 - 14, New Orleans, LA, USA. Red Hook, NY, USA: Curran Associates, 2021: 2641 -2653.
[24] SPERDUTI A, STARITA A. Supervised neural
networks for the classification of structures. IEEE Transactions on
Neural Networks, 1997, 8(3): 714 -735.
[25] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al.
Dropout: a simple way to prevent neural networks from
overfitting. The Journal of Machine Learning Research, 2014, 15
(1): 1929 - 1958.
[26] CHEN M, WEI Z W, HUANG Z F, et al. Simple and
deep graph convolutional networks. Proceedings of the 37th
International Conference on Machine Learning (ICML'20): Part 3,
2020, Jul 13 - 18, Online. Red Hook, NY, USA: Curran
Associates, 2020: 1703 -1713.
[27] RUIZ L, GAMA F, RIBEIRO A. Gated graph
recurrent neural networks. IEEE Transactions on Signal Processing,
2020, 68: 6303 -6318.
[28] CAO S S, LU W, XU Q K. GraRep: Learning graph representations with global structural
information. Proceedings of the 24th ACM International on Conference on
Information and Knowledge Management ( CIKM'15 ), 2015, Oct 18 -
23, Melbourne, Australia. New York, NY, USA: ACM, 2015: 891 -900.
[29] LIU K, XUE F, HONG R. RGCF: refined graph
convolution collaborative filtering with concise and
expressive embedding. Intelligent Data Analysis, 2022, 26(2): 427 -445.
[30] CHIANG W L, LIU X, SI S, et al. Cluster-GCN:
an efficient algorithm for training deep and large graph
convolutional networks.
Proceedings of the 25th ACM SIGKDD International
Conference on Knowledge Discovery & Data Mining (KDD '19),
2019, Aug 4 - 8, Anchorage, AK, USA. New York, NY, USA: ACM,
2019: 257 -266.
[31] LIU M, GAO H, JI S. Towards deeper graph
neural networks. Proceedings of the 26th ACM SIGKDD International
Conference on
Knowledge Discovery & Data Mining (KDD'20),
2020, Jul 6 - 10, Virtual Event, CA, USA. New York, NY, USA: ACM, 2020: 338 -348.
[32] GASTEIGER J, BOJCHEVSKI A, GÜNEMANN S.
Predict then propagate: graph neural networks meet personalized
PageRank. Proceedings of the 6th International Conference on
Learning Representations (ICLR'18), 2018, Apr 30 - May 3,
Vancouver, Canada. 2018: 1 -11.
[33] XU K, LI C, TIAN Y, et al. Representation
learning on graphs with jumping knowledge networks. Proceedings of
the 35th International Conference on Machine Learning (ICML'18),
2018, Jul 10 -15, Stockholm, Sweden. Red Hook, NY, USA:
Curran Associates, 2018: 5453 -5462.
[34] ZHAO J, DONG Y, TANG J, et al. Generalizing
graph convolutional networks via heat kernel.
Proceedings of the 9th International Conference on Learning Representations(ICLR'21), 2021, May 4 -8, Vienna, Austria. 2021: 553 -562.
[35] SEN P, GETOOR L. Link-based classification.
London, UK: Springer, 2005: 189 -207.
[36] SEN P, NAMATA G, BILGIC M, et al. Collective
classification in network data. AI Magazine, 2008, 29(3): 93 -93.
[37] TANG J, QU M, WANG M Z, et al. LINE: Large-scale information network embedding. Proceedings of the
24th International Conference on World Wide Web (WWW'15),
2015, May 18 -22, Florence, Italy. Geneva, Switzerland:
International World Wide Web Conferences Steering Committee,
2015: 1067 - 1077.
[38] VELICKOVI'C P, FEDUS W, HAMILTON W L, et al.
Deep graph infomax. Proceedings of the 7th
International Conference on Learning Representations ( ICLR'19), 2019, May 6 -
9, New Orleans, LA, USA. 2019: 1 -17.
[39] WU F L, ZHANG Y T, SOUZA A H, et al.
Simplifying graph convolutional networks. Proceedings of the 36th
International Conference on Machine Learning (ICML'19), 2019,
Jun 10 -13, Long Beach, CA, USA. Red Hook, NY, USA: Curran Associates, 2019: 6861 -6871.
|
