Deep Attributed Network Representation Learning via Attribute Enhanced
  Neighborhood
  Attributed network representation learning aims at learning node embeddings
by integrating network structure and attribute information. It is a challenge
to fully capture the microscopic structure and the attribute semantics
simultaneously, where the microscopic structure includes the one-step, two-step
and multi-step relations, indicating the first-order, second-order and
high-order proximity of nodes, respectively. In this paper, we propose a deep
attributed network representation learning via attribute enhanced neighborhood
(DANRL-ANE) model to improve the robustness and effectiveness of node
representations. The DANRL-ANE model adopts the idea of the autoencoder, and
expands the decoder component to three branches to capture different order
proximity. We linearly combine the adjacency matrix with the attribute
similarity matrix as the input of our model, where the attribute similarity
matrix is calculated by the cosine similarity between the attributes based on
the social homophily. In this way, we preserve the second-order proximity to
enhance the robustness of DANRL-ANE model on sparse networks, and deal with the
topological and attribute information simultaneously. Moreover, the sigmoid
cross-entropy loss function is extended to capture the neighborhood character,
so that the first-order proximity is better preserved. We compare our model
with the state-of-the-art models on five real-world datasets and two network
analysis tasks, i.e., link prediction and node classification. The DANRL-ANE
model performs well on various networks, even on sparse networks or networks
with isolated nodes given the attribute information is sufficient.
