Supervised Graph Node-based Classification Algorithm

A supervised graph node-based classification algorithm is a data-driven link prediction algorithm that is a supervised multiclass classification algorithm and can be implemented into a Supervised Link Prediction System (to solve a supervised link prediction task).

• AKA: Supervised Graph Node Linking Algorithm.
• Context:
  • It can range from being a Fully-Supervised Multiclass Classification Algorithm to being a Semi-Supervised Multiclass Classification Algorithm.
  • It can range from being a Single Machine Supervised Multiclass Classification Algorithm to being a Binary-based Supervised Multiclass Classification Algorithm.
• Example(s):
  • Supervised Concept Mention Linking Algorithm (a Directed Edge Linking Task).
  • a Supervised Taxonomy Node-based Classification Algorithm.
  • …
• Counter-Example(s):
  • an Unsupervised Link Prediction Algorithm.
  • a Supervised Sequence Tagging Algorithm.
  • a Heuristic Link Prediction Algorithm.
• See: Directed Graph Edge.

---

---

References

2011

• (Menon & Elkan, 2011) ⇒ Aditya Krishna Menon, and Charles Elkan. (2011). “Link Prediction via Matrix Factorization.” In: Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II. ISBN:978-3-642-23782-9
  • QUOTE: At a high level, existing link prediction models fall into two classes: unsupervised and supervised. Unsupervised models compute scores for pairs of nodes based on topological properties of the graph. ... Supervised models, on the other hand, attempt to be directly predictive of link behaviour by learning a parameter vector θ via ... The choice of these terms depends on the type of model.
    We list some popular approaches: ...

2009

• (Li & Chen, 2009) ⇒ Xin Li, and Hsinchun Chen. (2009). “Recommendation as link prediction: a graph kernel-based machine learning approach.” In: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries. doi:10.1145/1555400.1555433

2008

• (Scripps et al., 2008) ⇒ Jerry Scripps, Pang-Ning Tan, Feilong Chen, and Abdol-Hossein Esfahanian. (2008). “A Matrix Alignment Approach for Link Prediction.” In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008).

2007

• (Wang et al., 2007) ⇒ Chao Wang, Venu Satuluri, and Srinivasan Parthasarathy. (2007). “Local Probabilistic Models for Link Prediction.” In: Proceedings of Seventh IEEE International Conference on Data Mining (ICDM 2007). [doi:10.1109/ICDM.2007.108]

2006

• (Al Hasan et al., 2006) ⇒ Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. (2006). “Link Prediction Using Supervised Learning.” In: SDM’06: Workshop on Link Analysis, Counter-terrorism and Security.
• (Kashima & Abe, 2006) ⇒ Hisashi Kashima, and Naoki Abe. (2006). “A Parameterized probabilistic model of network evolution for supervised link prediction.” In: Proceedings of Sixth IEEE International Conference on Data Mining (ICDM 2006).

2003

• (Taskar et al., 2003) ⇒ Ben Taskar, Ming-Fai Wong, Pieter Abbeel and Daphne Koller. (2003). “Link Prediction in Relational Data.” In: Neural Information Processing Systems Conference (NIPS 2003)
• (Liben-Nowell & Kleinberg) ⇒ David Liben-Nowell, and Jon Kleinberg. (2003). “The Link Prediction Problem for Social Networks.” In: Proceedings of the twelfth International Conference on Information and knowledge management (CIKM 2003). doi:10.1145/956863.956972
• (Popescul & Ungar, 2003) ⇒ Alexandrin Popescul, and Lyle H. Ungar. (2003). “Statistical relational learning for link prediction.” In: Proceedings of IJCAI03 Workshop on Learning Statistical Models from Relational Data

2000

• (Sarukkai, 2000) ⇒ Ramesh R. Sarukkai. (2000). “Link Prediction and Path Analysis Using Markov Chains.” doi:10.1016/S1389-1286(00)00044-X

---