  In network embedding, random walks play a fundamental role in preserving
network structures. However, random walk based embedding methods have two
limitations. First, random walk methods are fragile when the sampling frequency
or the number of node sequences changes. Second, in disequilibrium networks
such as highly biases networks, random walk methods often perform poorly due to
the lack of global network information. In order to solve the limitations, we
propose in this paper a network diffusion based embedding method. To solve the
first limitation, our method employs a diffusion driven process to capture both
depth information and breadth information. The time dimension is also attached
to node sequences that can strengthen information preserving. To solve the
second limitation, our method uses the network inference technique based on
cascades to capture the global network information. To verify the performance,
we conduct experiments on node classification tasks using the learned
representations. Results show that compared with random walk based methods,
diffusion based models are more robust when samplings under each node is rare.
We also conduct experiments on a highly imbalanced network. Results shows that
the proposed model are more robust under the biased network structure.
