18 citations · 24 across the 5 of their papers we have counts for
5 papers
GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs
Shima Khoshraftar, Niaz Abedini, Amir Hajian
The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in t…
A Survey on Graph Representation Learning Methods
Shima Khoshraftar, Aijun An
Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capt…
Dynamic Graph Embedding via LSTM History Tracking
Shima Khoshraftar, Sedigheh Mahdavi, Aijun An +2
Many real world networks are very large and constantly change over time. These dynamic networks exist in various domains such as social networks, traffic networks and biological in…
Dynamic Joint Variational Graph Autoencoders
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. Many rea…
dynnode2vec: Scalable Dynamic Network Embedding
Sedigheh Mahdavi, Shima Khoshraftar, Aijun An
Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic w…