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.
|Original language||English (US)|
|Title of host publication||Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018|
|Editors||Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Jan 22 2019|
|Event||2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States|
Duration: Dec 10 2018 → Dec 13 2018
|Name||Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018|
|Conference||2018 IEEE International Conference on Big Data, Big Data 2018|
|Period||12/10/18 → 12/13/18|
Bibliographical noteFunding Information:
This work was supported by NSF 1711471, 1500713, and 1442686.
1This work was supported by NSF 1711471, 1500713, and 1442686.
© 2018 IEEE.
- Label Propagation
- Markov Chains
- Random Walks