AdaDIF: Adaptive Diffusions for Efficient Semi-supervised Learning over Graphs
| Autor: | Georgios B. Giannakis, Athanasios N. Nikolakopoulos, Dimitris Berberidis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Theoretical computer science
Markov chain Computer science 020206 networking & telecommunications 02 engineering and technology Semi-supervised learning 010501 environmental sciences Network topology Random walk 01 natural sciences Graph law.invention PageRank law 0202 electrical engineering electronic engineering information engineering Heat kernel 0105 earth and related environmental sciences |
| Zdroj: | IEEE BigData |
| Popis: | Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, that can be specific to the underlying graph, and potentially different for each class. The present work introduces a disciplined, data-efficient approach to learning class-specific diffusion functions adapted to the underlying network topology. The novel learning approach leverages the notion of "landing probabilities" of class-specific random walks, which can be computed efficiently, thereby ensuring scalability to large graphs. This is supported by rigorous analysis of the properties of the model as well as the proposed algorithms. Classification tests on real networks demonstrate that adapting the diffusion function to the given graph and observed labels, significantly improves the performance over fixed diffusions; reaching—and many times surpassing—the classification accuracy of computationally heavier state-of-the-art competing methods, that rely on node embeddings and deep neural networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|