2000 character limit reached
Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks (2401.05468v1)
Published 10 Jan 2024 in cs.SI, cs.AI, and cs.LG
Abstract: This paper introduces a new problem in the field of graph mining and social network analysis called new node prediction. More technically, the task can be categorized as zero-shot out-of-graph all-links prediction. This challenging problem aims to predict all links from a new, isolated, and unobserved node that was previously disconnected from the graph. Unlike classic approaches to link prediction (including few-shot out-of-graph link prediction), this problem presents two key differences: (1) the new node has no existing links from which to extract patterns for new predictions; and (2) the goal is to predict not just one, but all the links of this new node, or at least a significant part of them. Experiments demonstrate that an architecture based on Deep Graph Neural Networks can learn to solve this challenging problem in a bibliographic citation network.
- S. J. Ahn and M. Kim. Variational graph normalized autoencoders. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management, CIKM ’21, page 2827–2831, New York, NY, USA, 2021. Association for Computing Machinery.
- Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
- A.-L. Barabási and R. Albert. Emergence of Scaling in Random Networks. Science, 286(5439):509–512, 1999.
- E. Bisong. Google Colaboratory, pages 59–64. Apress, Berkeley, CA, 2019.
- Meta-graph: Few shot link prediction via meta learning. CoRR, abs/1912.09867, 2019.
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 257–266, 2019.
- P. Erdös and A. Rényi. On random graphs i. Publicationes Mathematicae Debrecen, 6:290, 1959.
- An open challenge for inductive link prediction on knowledge graphs, 2022.
- A. Grover and J. Leskovec. node2vec: Scalable feature learning for networks. CoRR, abs/1607.00653, 2016.
- Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach. CoRR, abs/1706.05674, 2017.
- Inductive Representation Learning on Large Graphs. In Proceedings of the International Conference on Neural Information Processing Systems (NIPS), pages 1025–1035, 2017.
- Exploring the limits of few-shot link prediction in knowledge graphs. CoRR, abs/2102.03419, 2021.
- D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
- T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2017.
- D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of The American Society For Information Science and Technology, 58(7):1019–1031, 2007.
- C. Mavromatis and G. Karypis. Graph infoclust: Leveraging cluster-level node information for unsupervised graph representation learning. CoRR, abs/2009.06946, 2020.
- A review of relational machine learning for knowledge graphs: From multi-relational link prediction to automated knowledge graph construction. CoRR, abs/1503.00759, 2015.
- Neural link prediction with walk pooling. CoRR, abs/2110.04375, 2021.
- Deepwalk: Online learning of social representations. CoRR, abs/1403.6652, 2014.
- Generative adversarial zero-shot relational learning for knowledge graphs. CoRR, abs/2001.02332, 2020.
- Y. Song and D. Wang. Learning on graphs with out-of-distribution nodes. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’22, page 1635–1645, New York, NY, USA, 2022. Association for Computing Machinery.
- T. Ucar. NESS: learning node embeddings from static subgraphs. CoRR, abs/2303.08958, 2023.
- Graph Attention Networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
- Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1):396–413, 2020.
- Logic attention based neighborhood aggregation for inductive knowledge graph embedding. CoRR, abs/1811.01399, 2018.
- Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12):2724–2743, 2017.
- Few-shot learning on graphs. In L. D. Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 5662–5669. ijcai.org, 2022.
- M. Zhang and Y. Chen. Link prediction based on graph neural networks. In S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 5171–5181, 2018.
- Graph Neural Networks: A Review of Methods and Applications. AI Open 1, pages 57–81, 2020.