Title: Network Representation Learning: A Survey,
Abstract: With the widespread use of information technologies, information networks are
becoming increasingly popular to capture complex relationships across various
disciplines, such as social networks, citation networks, telecommunication
networks, and biological networks. Analyzing these networks sheds light on
different aspects of social life such as the structure of societies,
information diffusion, and communication patterns. In reality, however, the
large scale of information networks often makes network analytic tasks
computationally expensive or intractable. Network representation learning has
been recently proposed as a new learning paradigm to embed network vertices
into a low-dimensional vector space, by preserving network topology structure,
vertex content, and other side information. This facilitates the original
network to be easily handled in the new vector space for further analysis. In
this survey, we perform a comprehensive review of the current literature on
network representation learning in the data mining and machine learning field.
We propose new taxonomies to categorize and summarize the state-of-the-art
network representation learning techniques according to the underlying learning
mechanisms, the network information intended to preserve, as well as the
algorithmic designs and methodologies. We summarize evaluation protocols used
for validating network representation learning including published benchmark
datasets, evaluation methods, and open source algorithms. We also perform
empirical studies to compare the performance of representative algorithms on
common datasets, and analyze their computational complexity. Finally, we
suggest promising research directions to facilitate future study.