A Survey of the Link Prediction on Static and Temporal Knowledge Graph
Link prediction in knowledge graphs gradually plays an essential role in the field of research and application.
Through detecting latent connections, we can refine the knowledge in the graph, discover interesting relationships, answer user questions or make item suggestions. In this paper, we conduct a survey of the methods that are currently achieving good results in link prediction. Specially, we perform surveys on both static and temporal graphs. First, we divide the algorithms into groups based on the characteristic
representation of entities and relations. After that, we describe the original idea and analyze the key improvements. In each group, comparisons and investigation on the pros and cons of each method as well as their applications are made. Based on that, the correlation of the two graph types in link prediction is drawn. Finally, from the overview of the link prediction problem, we propose some directions to improve the models
for future studies
L. Ulanoff, “Google search just got 1,000 times smarter,” May 2012. [Online]. Available: https://mashable.com/2012/05/16/google-knowledge-graph/
E. W. Schneider, “Course modularization applied: The interface system and its implications for sequence control and data analysis.” 1973.
N. Noy, Y. Gao, A. Jain, A. Narayanan, A. Patterson, and J. Taylor, “Industry-scale knowledge graphs: lessons and challenges,” Communications of the ACM, vol. 62, no. 8, pp. 36–43, 2019.
D. Devarajan, “Happy birthday watson discovery,” 2017. [Online]. Available:
Q. He, B. Chen, and D. Agarwal, “Building the linkedin knowledge graph,” Engineering. linkedin. com, 2016.
S. E. Shimony, C. Domshlak, and E. Santos Jr, “Costsharing in bayesian knowledge bases,” arXiv preprint
arXiv:1302.1567, 2013.  A. Krishnan, “Making search easier,” Aug 2018. [Online]. Available: https://www.aboutamazon.com/news/innovationat-amazon/making-search-easier
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, “Dbpedia: A nucleus for a web of open data,” in The semantic web. Springer, 2007, pp. 722–735.
R. Speer, J. Chin, and C. Havasi, “Conceptnet 5.5: An open multilingual graph of general knowledge,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, 2017.
G. A. Miller, “Wordnet: a lexical database for english,” Communications of the ACM, vol. 38, no. 11, pp. 39–41,
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008, pp. 1247–1250.
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang, “Knowledge vault: A web-scale approach to probabilistic knowledge fusion,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 601–610.
F. Stefano, I. Finocchi, S. P. Ponzetto, and V. Paola, “Webisagraph: A very large hypernymy graph from a web corpus,” in Sixth Italian Conference on Computational Linguistics, 2019.
J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum, “Yago2: A spatially and temporally enhanced knowledge base from wikipedia,” Artificial Intelligence, vol. 194, pp. 28–61, 2013.
G. Bouchard, S. Singh, and T. Trouillon, “On approximate reasoning capabilities of low-rank vector spaces.” in AAAI spring symposia. Citeseer, 2015.
J. Ugander, B. Karrer, L. Backstrom, and C. Marlow, “The anatomy of the facebook social graph,” arXiv preprint arXiv:1111.4503, 2011.
B. Shi and T. Weninger, “Fact checking in heterogeneous information networks,” in Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 101–102.
D. Lukovnikov, A. Fischer, J. Lehmann, and S. Auer, “Neural network-based question answering over knowledge graphs on word and character level,” in Proceedings of the 26th international conference on World Wide Web, 2017, pp. 1211–1220.
B. Hachey, W. Radford, J. Nothman, M. Honnibal, and J. R. Curran, “Evaluating entity linking with wikipedia,”
Artificial intelligence, vol. 194, pp. 130–150, 2013.
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge
graphs,” Proceedings of the IEEE, vol. 104, no. 1, pp. 11– 33, 2015.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and S. Y. Philip, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Transactions on Neural Networks and Learning Systems, 2021.
S. Samanta and M. Pal, “Link prediction in social networks,” in Graph Theoretic Approaches for Analyzing
Large-Scale Social Networks. IGI Global, 2018, pp. 164– 172.
D. Oniani, G. Jiang, H. Liu, and F. Shen, “Constructing co-occurrence network embeddings to assist association extraction for covid-19 and other coronavirus infectious diseases,” Journal of the American Medical Informatics Association, vol. 27, no. 8, pp. 1259–1267, 2020.
G. Berlusconi, F. Calderoni, N. Parolini, M. Verani, and C. Piccardi, “Link prediction in criminal networks: A tool for criminal intelligence analysis,” PloS one, vol. 11, no. 4, p. e0154244, 2016.
D. Dalal, M. Arcan, and P. Buitelaar, “Enhancing multiplechoice question answering with causal knowledge,” in Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 2021, pp. 70–80.
L. Lu and T. Zhou, “Link prediction in complex networks: ¨ A survey,” Physica A: statistical mechanics and its applications, vol. 390, no. 6, pp. 1150–1170, 2011.
V. Martínez, F. Berzal, and J.-C. Cubero, “A survey of link prediction in complex networks,” ACM computing surveys (CSUR), vol. 49, no. 4, pp. 1–33, 2016.
Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 12, pp. 2724–2743, 2017.
H. Paulheim, “Knowledge graph refinement: A survey of approaches and evaluation methods,” Semantic web, vol. 8, no. 3, pp. 489–508, 2017.
P. Goyal and E. Ferrara, “Graph embedding techniques, applications, and performance: A survey,” Knowledge-Based Systems, vol. 151, pp. 78–94, 2018.
S. Haghani and M. R. Keyvanpour, “A systemic analysis of link prediction in social network,” Artificial Intelligence Review, vol. 52, no. 3, pp. 1961–1995, 2019.
G. A. Gesese, R. Biswas, and H. Sack, “A comprehensive survey of knowledge graph embeddings with literals: Techniques and applications.” in DL4KG@ ESWC, 2019, pp. 31–40.
R. Biswas, “Embedding based link prediction for knowledge graph completion,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, pp. 3221–3224.
Z. Chen, Y. Wang, B. Zhao, J. Cheng, X. Zhao, and Z. Duan, “Knowledge graph completion: A review,” IEEE
Access, vol. 8, pp. 192 435–192 456, 2020.
A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. de Melo, C. Gutierrez, J. E. L. Gayo, S. Kirrane,
S. Neumaier, A. Polleres et al., “Knowledge graphs,” arXiv preprint arXiv:2003.02320, 2020.
S. Arora, “A survey on graph neural networks for knowledge graph completion,” arXiv preprint arXiv:2007.12374, 2020.
Y. Dai, S. Wang, N. N. Xiong, and W. Guo, “A survey on knowledge graph embedding: Approaches, applications and benchmarks,” Electronics, vol. 9, no. 5, p. 750, 2020.
A. Kumar, S. S. Singh, K. Singh, and B. Biswas, “Link prediction techniques, applications, and performance: A survey,” Physica A: Statistical Mechanics and its Applications, vol. 553, p. 124289, 2020.
A. Divakaran and A. Mohan, “Temporal link prediction: a survey,” New Generation Computing, pp. 1–46, 2019.
B. Andreas and N. Helmut, “The knowledge graph cookbook recipes that work,” Edition mono/monochrom, Vienna, 2020.
D. Fensel, U. S¸ims¸ek, K. Angele, E. Huaman, E. Karle, ¨ O. Panasiuk, I. Toma, J. Umbrich, and A. Wahler, Knowledge Graphs. Springer, 2020.
M. Wang, L. Qiu, and X. Wang, “A survey on knowledge graph embeddings for link prediction,” Symmetry, vol. 13, no. 3, p. 485, 2021.
M. Al Hasan and M. J. Zaki, “A survey of link prediction in social networks,” in Social network data analytics.
Springer, 2011, pp. 243–275.
A. Rossi, D. Barbosa, D. Firmani, A. Matinata, and P. Merialdo, “Knowledge graph embedding for link prediction: A comparative analysis,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 2, pp. 1–49, 2021.
M. Kejriwal, C. A. Knoblock, and P. Szekely, Knowl-edge Graphs: Fundamentals, Techniques, and Applications.
MIT Press, 2021.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling
multi-relational data,” in Neural Information Processing Systems (NIPS), 2013, pp. 1–9.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1, 2014.
B. Yang, W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in
knowledge bases,” arXiv preprint arXiv:1412.6575, 2014.
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in International Conference on Machine Learning. PMLR, 2016, pp. 2071–2080.
S. S. Dasgupta, S. N. Ray, and P. Talukdar, “Hyte: Hyperplane-based temporally aware knowledge graph embedding,” in Proceedings of the 2018 conference on empirical methods in natural language processing, 2018, pp. 2001–2011.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, and J. Lehmann, “Tero: A time-aware knowledge graph embedding via temporal rotation,” arXiv preprint arXiv:2010.01029, 2020.
A. Garcia-Duran, A. Bordes, and N. Usunier, “Composing relationships with translations,” Ph.D. dissertation, CNRS, Heudiasyc, 2015.
N. M. A. Ibrahim and L. Chen, “Link prediction in dynamic social networks by integrating different types of information,” Applied Intelligence, vol. 42, no. 4, pp. 738–750, 2015.
˙ I. Gunes¸, S¸. G ¨ und ¨ uz- ¨ O¨ g˘ud ¨ uc ¨ u, and Z. C¸ ataltepe, “Link ¨ prediction using time series of neighborhood-based node similarity scores,” Data Mining and Knowledge Discovery, vol. 30, no. 1, pp. 147–180, 2016.
B. Moradabadi and M. R. Meybodi, “Link prediction based on temporal similarity metrics using continuous action set learning automata,” Physica a: statistical mechanics and its applications, vol. 460, pp. 361–373, 2016.
Y. Dhote, N. Mishra, and S. Sharma, “Survey and analysis of temporal link prediction in online social networks,” in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2013, pp. 1178–1183.
T. Jiang, T. Liu, T. Ge, L. Sha, S. Li, B. Chang, and Z. Sui, “Encoding temporal information for time-aware
link prediction,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016, pp. 2350–2354.
R. Goel, S. M. Kazemi, M. Brubaker, and P. Poupart, “Diachronic embedding for temporal knowledge graph
completion,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, 2020, pp. 3988–3995.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, and J. Lehmann, “Temporal knowledge graph embedding model
based on additive time series decomposition,” arXiv preprint arXiv:1911.07893, 2019.
H. Cai, V. W. Zheng, and K. C.-C. Chang, “A comprehensive survey of graph embedding: Problems, techniques, and applications,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 9, pp. 1616–1637, 2018.
Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “Rotate: Knowledge graph embedding by relational rotation in complex space,” arXiv preprint arXiv:1902.10197, 2019.
G. Asefa Gesese, R. Biswas, M. Alam, and H. Sack, “A survey on knowledge graph embeddings with literals:
Which model links better literal-ly?” arXiv e-prints, pp. arXiv–1910, 2019.
T. Li, J. Zhang, S. Y. Philip, Y. Zhang, and Y. Yan, “Deep dynamic network embedding for link prediction,” IEEE
Access, vol. 6, pp. 29 219–29 230, 2018.
R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base
completion,” in Advances in neural information processing systems. Citeseer, 2013, pp. 926–934.
B. Kotnis and V. Nastase, “Analysis of the impact of negative sampling on link prediction in knowledge graphs,” arXiv preprint arXiv:1708.06816, 2017.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. Hruschka, and T. Mitchell, “Toward an architecture for never-ending language learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, no. 1, 2010.
Y. Peng and J. Zhang, “Lineare: Simple but powerful knowledge graph embedding for link prediction,” in 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020, pp. 422–431.
K. Toutanova and D. Chen, “Observed versus latent features for knowledge base and text inference,” in Proceedings of the 3rd workshop on continuous vector space models and their compositionality, 2015, pp. 57–66.
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2d knowledge graph embeddings,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
F. Mahdisoltani, J. Biega, and F. Suchanek, “Yago3: A knowledge base from multilingual wikipedias,” in 7th biennial conference on innovative data systems research. CIDR Conference, 2014.
M. Nickel, L. Rosasco, and T. Poggio, “Holographic embeddings of knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, 2016.
B. Shi and T. Weninger, “Open-world knowledge graph completion,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
A. Breit, S. Ott, A. Agibetov, and M. Samwald, “Openbiolink: a benchmarking framework for large-scale biomedical link prediction,” Bioinformatics, vol. 36, no. 13, pp. 4097–4098, 2020.
M. Zitnik, R. Sosic, and J. Leskovec, “Biosnap datasets: Stanford biomedical network dataset collection,” Note: http://snap. stanford. edu/biodata Cited by, vol. 5, no. 1, 2018.
M. Nickel, V. Tresp, and H.-P. Kriegel, “A three-way model for collective learning on multi-relational data,” in Icml, 2011.
S. M. Kazemi and D. Poole, “Simple embedding for link prediction in knowledge graphs,” arXiv preprint
I. Balazevi ˇ c, C. Allen, and T. M. Hospedales, “Tucker: Ten´sor factorization for knowledge graph completion,” arXiv preprint arXiv:1901.09590, 2019.
S. Sonkar, A. Katiyar, and R. G. Baraniuk, “Neptune: Neural powered tucker network for knowledge graph completion,” arXiv preprint arXiv:2104.07824, 2021.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” arXiv preprint arXiv:1310.4546, 2013.
P. Minervini, C. d’Amato, N. Fanizzi, and F. Esposito, “Efficient learning of entity and predicate embeddings for link prediction in knowledge graphs.” in URSW@ ISWC, 2015, pp. 26–37.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1, 2015.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers), 2015, pp. 687–696.
G. Ji, K. Liu, S. He, and J. Zhao, “Knowledge graph completion with adaptive sparse transfer matrix,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, 2016.
T. Ebisu and R. Ichise, “Toruse: Knowledge graph embedding on a lie group,” in Proceedings of the AAAI
Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
S. Zhang, Y. Tay, L. Yao, and Q. Liu, “Quaternion knowledge graph embeddings,” arXiv preprint rXiv:1904.10281, 2019.
H. Lu and H. Hu, “Dense: An enhanced non-abelian group representation for knowledge graph embedding,” arXiv preprint arXiv:2008.04548, 2020.
X. Jiang, Q. Wang, and B. Wang, “Adaptive convolution for multi-relational learning,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 978–987.
D. Q. Nguyen, T. D. Nguyen, D. Q. Nguyen, and D. Phung, “A novel embedding model for knowledge base completion based on convolutional neural network,” arXiv preprint arXiv:1712.02121, 2017.
G. Stoica, O. Stretcu, E. A. Platanios, T. Mitchell, and B. Póczos, “Contextual parameter generation for knowledge graph link prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 03, 2020, pp. 3000–3008.
S. Vashishth, S. Sanyal, V. Nitin, N. Agrawal, and P. Talukdar, “Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 03, 2020, pp. 3009–3016.
D. Nathani, J. Chauhan, C. Sharma, and M. Kaul, “Learning attention-based embeddings for relation prediction in knowledge graphs,” arXiv preprint arXiv:1906.01195, 2019.
K. Ji, B. Hui, and G. Luo, “Graph attention networks with local structure awareness for knowledge graph completion,” IEEE Access, 2020.
X. Liu, H. Tan, Q. Chen, and G. Lin, “Ragat: Relation aware graph attention network for knowledge graph completion,” IEEE Access, vol. 9, pp. 20 840–20 849, 2021.
L. Yao, C. Mao, and Y. Luo, “Kg-bert: Bert for knowledge graph completion,” arXiv preprint arXiv:1909.03193, 2019.
B. Wang, T. Shen, G. Long, T. Zhou, Y. Wang, and Y. Chang, “Structure-augmented text representation learning for efficient knowledge graph completion,” in Proceedings of the Web Conference 2021, 2021, pp. 1737–1748.
L. Cai and W. Y. Wang, “Kbgan: Adversarial learning for knowledge graph embeddings,” arXiv preprint
C. Meilicke, M. W. Chekol, D. Ruffinelli, and H. Stuckenschmidt, “Anytime bottom-up rule learning for knowledge graph completion.” in IJCAI, 2019, pp. 3137–3143.
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo, “Jointly embedding knowledge graphs and logical rules,” in Proceedings of the 2016 conference on empirical methods in natural language processing, 2016, pp. 192–202.
J. Yuan, N. Gao, and J. Xiang, “Transgate: knowledge graph embedding with shared gate structure,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 3100–3107.
W. Zhang, B. Paudel, L. Wang, J. Chen, H. Zhu, W. Zhang, A. Bernstein, and H. Chen, “Iteratively learning embeddings and rules for knowledge graph reasoning,” in The World Wide Web Conference, 2019, pp. 2366–2377.
M. Nayyeri, C. Xu, J. Lehmann, and H. S. Yazdi, “Logicenn: A neural based knowledge graphs embedding model with logical rules,” arXiv preprint arXiv:1908.07141, 2019.
W. Xiong, T. Hoang, and W. Y. Wang, “Deeppath: A reinforcement learning method for knowledge graph reasoning,” arXiv preprint arXiv:1707.06690, 2017.
R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishnamurthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” arXiv preprint arXiv:1711.05851, 2017.
C. Raymond, R. M. Horton, J. Zscheischler, O. Martius, A. AghaKouchak, J. Balch, S. G. Bowen, S. J. Camargo,
J. Hess, K. Kornhuber et al., “Understanding and managing connected extreme events,” Nature climate change, vol. 10, no. 7, pp. 611–621, 2020.
D. M. Dunlavy, T. G. Kolda, and E. Acar, “Temporal link prediction using matrix and tensor factorizations,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 5, no. 2, pp. 1–27, 2011.
R. A. Harshman et al., “Foundations of the parafac procedure: Models and conditions for an" explanatory" multimodal factor analysis,” 1970.
H. A. Kiers, “Towards a standardized notation and terminology in multiway analysis,” Journal of Chemometrics: A Journal of the Chemometrics Society, vol. 14, no. 3, pp. 105–122, 2000.
T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM review, vol. 51, no. 3, pp. 455–500, 2009.
V. Ouzienko, Y. Guo, and Z. Obradovic, “Prediction of attributes and links in temporal social networks.” in ECAI, 2010, pp. 1121–1122.
P. Sarkar, D. Chakrabarti, and M. Jordan, “Nonparametric link prediction in dynamic networks,” arXiv preprint arXiv:1206.6394, 2012.
T. Wang, X.-S. He, M.-Y. Zhou, and Z.-Q. Fu, “Link prediction in evolving networks based on popularity of
nodes,” Scientific reports, vol. 7, no. 1, pp. 1–10, 2017.
T. Wu, C.-S. Chang, and W. Liao, “Tracking network evolution and their applications in structural network analysis,” IEEE Transactions on Network Science and Engineering, vol. 6, no. 3, pp. 562–575, 2018.
G. G. Chowdhury, Introduction to modern information retrieval. Facet publishing, 2010.
L. Zhou, Y. Yang, X. Ren, F. Wu, and Y. Zhuang, “Dynamic network embedding by modeling triadic closure process,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim, “Continuous-time dynamic network
embeddings,” in Companion Proceedings of the The Web Conference 2018, 2018, pp. 969–976.
M. Rahman, T. K. Saha, M. A. Hasan, K. S. Xu, and C. K. Reddy, “Dylink2vec: Effective feature representation
for link prediction in dynamic networks,” arXiv preprint arXiv:1804.05755, 2018.
W. Yu, W. Cheng, C. C. Aggarwal, H. Chen, and W. Wang, “Link prediction with spatial and temporal consistency in dynamic networks.” in IJCAI, 2017, pp. 3343–3349.
M. Fey, J. E. Lenssen, F. Weichert, and H. Muller, “Splinecnn: Fast geometric deep learning with continuous
b-spline kernels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp.
M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma et al., “Deep graph library:
Towards efficient and scalable deep learning on graphs.” 2019.
N. Talasu, A. Jonnalagadda, S. S. A. Pillai, and J. Rahul, “A link prediction based approach for recommendation systems,” in 2017 international conference on advances in computing, communications and informatics (ICACCI). IEEE, 2017, pp. 2059–2062.
H. Y. Yuen and J. Jansson, “Better link prediction for protein-protein interaction networks,” in 2020 IEEE 20th
International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2020, pp. 53–60.
Y. Zheng, B. Wang, W. Lou, and Y. T. Hou, “Privacypreserving link prediction in decentralized online social
networks,” in European Symposium on Research in Computer Security. Springer, 2015, pp. 61–80.
F. Folino and C. Pizzuti, “Link prediction approaches for disease networks,” in International Conference on Information Technology in Bio-and Medical Informatics. Springer, 2012, pp. 99–108.
J. Jiang, L.-P. Liu, and S. Hassoun, “Learning graph representations of biochemical networks and its application to enzymatic link prediction,” Bioinformatics, vol. 37, no. 6, pp. 793–799, 2021.