Voluminous works have been implemented to exploit content-enhanced network embedding models, with little focus on the labelled information of nodes.
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.
Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community.
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering.
More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations.