Network Representation Learning with Attributes and Heterogeneity

Network Representation Learning (NRL) aims at learning a low-dimensional latent representation of nodes in a graph while preserving the graph information. The learned representation enables to easily and efficiently perform various machine learning tasks. Graphs are often associated with diverse and rich information such as attributes that play an important role in the formation of the network. Thus, it is imperative to exploit this information to complement the structure information and learn a better representation. This requires designing effective models which jointly leverage structure and attribute information. In case of a heterogeneous network, NRL methods should preserve the different relation types. Towards this goal, this thesis proposes two models to learn a representation of attributed graphs and one model for learning representation in a heterogeneous network. In general, our approach is based on appropriately modeling the relation between graphs and attributes on one hand, between heterogeneous nodes on the other, executing a large collection of random walks over such graphs, and then applying off-the-shelf learning techniques to the data obtained from the walks. All our contributions are evaluated against a large number of state-of-the-art algorithms, on several well-known datasets, obtaining better results.