We emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic Exponential Family Graph Embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.
An implementation of the project in C++ can be reached at the Github repository.
A. Çelikkanat and F. D. Malliaros, Exponential Family Graph Embeddings, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York City, New York, 2020.
A. Çelikkanat and F. D. Malliaros, Learning Node Embeddings with Exponential Family Distributions, Thirty-third Conference on Neural Information Processing Systems - Workshop on Graph Representation Learning, Vancouver, Canada, 2019