Supervised Graph Node Prediction Task

A Supervised Graph Node Prediction Task is a data-driven graph node-based classification task that is a Graph Node-based Classification Task (with labeled graph nodes).

• AKA: Supervised Graph Node Linking, Supervised Graph Node Prediction.
• Context:
  • Input: Labeled Link Training Record.
  • It can be solved by a Supervised Link Prediction System (that implements a Supervised Link Prediction algorithm).
  • It can be solved by a Supervised Graph Node Classification System (that implements a Supervised Graph Node Classification Algorithm).
  • It can range from being a Fully-Supervised Link Prediction Task to being a Semi-Supervised Link Prediction Task.
  • It can range from being a Supervised Directed Link Prediction Task to being a Supervised Undirected Link Prediction Task.
• Example(s):
  • a Supervised Reference Resolution Task, such as a Supervised Concept Mention Linking Task.
  • a Supervised Taxonomy Node Linking Task, such as a supervised product taxonomy linking task.
  • …
• Counter-Example(s):
  • a Supervised Graph Edge Classification Task.
  • an Unsupervised Graph Node Classification Task.
  • a Supervised Graph Node-based Classification Task.
• See: Supervised Graph Classification Task.

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References

2010

• (Aggarwal & Wang, 2010) ⇒ Charu C. Aggarwal, and Haixun Wang. (2010b). “Graph Data Management and Mining: A Survey of Algorithms and Applications.” In: (Aggarwal & Wang, 2010a) doi:10.1007/978-1-4419-6045-0_2
  • Label Propagation. A subset of nodes in a graph are labeled. The task is to learn a model from the labeled nodes and use the model to classify the unlabeled nodes.
  • Label Propagation. The concept of label or belief propagation [174, 209, 210] is a fundamental technique which is used in order to leverage graph structure in the context of classification in a number of relational domains. The scenario of label propagation [44] occurs in many applications. As an example, social network analysis is being used as a mean for targeted marketing.

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