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Semi-Supervised Learning With Graphs

This document explores various techniques in semi-supervised learning (SSL) using graph structures. It covers fundamental concepts such as PageRank, Random Walk with Restart (RWR), and approximate Personalized PageRank. Additional methods such as MultiRankWalk and Modified Adsorption are discussed, along with approaches to extract subcommunities for sampling. The optimization of SSL on graphs is analyzed, including its application on non-graph datasets and unsupervised learning. The outline also addresses mathematical optimization techniques relevant to SSL methods.

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Semi-Supervised Learning With Graphs

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  1. Semi-Supervised Learning With Graphs William Cohen

  2. Section outline: SSL and Graphs • PageRank - how to scale it • RWR/Personalized PageRank • approximate Personalized PageRank • plus a “sweep” - extract a subcommunity in a graph, for sampling purposes • RWR for SSL classification of network data • MultiRankWalk method • “Harmonic field”/wvRN/Co-EM baseline • Modified Adsorption and SSL • SSL on graphs as an optimization problem • Unsupervised learning on graphs • Learning on graphs for non-graph datasets • unsupervised and semi-supervised

  3. Modified Adsorption

  4. More on SSL on graphsfrom ParthaTalukdar

  5. How to do this minimization? • First, differentiate to find min is at • Jacobi method: • To solve Ax=b for x • Iterate: • … or:

  6. Graph: connect each documentto its K nearest neighbors

  7. Graph: connect each documentto its K nearest neighbors

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