A Survey on Graph Representation Learning Methods

no code implementations • 4 Apr 2022 • Shima Khoshraftar, Aijun An

This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection.

Anomaly Detection Graph Embedding +4

Dynamic Graph Embedding via LSTM History Tracking

no code implementations • 5 Nov 2019 • Shima Khoshraftar, Sedigheh Mahdavi, Aijun An, Yonggang Hu, Junfeng Liu

To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space.

Anomaly Detection Dynamic graph embedding +3

Dynamic Joint Variational Graph Autoencoders

no code implementations • 4 Oct 2019 • Sedigheh Mahdavi, Shima Khoshraftar, Aijun An

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization.

Clustering Graph Clustering +4

dynnode2vec: Scalable Dynamic Network Embedding

no code implementations • 6 Dec 2018 • Sedigheh Mahdavi, Shima Khoshraftar, Aijun An

Node2vec is a random walk based embedding method for static networks.

Graph Embedding Network Embedding