3 papers
cs.LG2024
Rethinking Node Representation Interpretation through Relation Coherence
Ying-Chun Lin, Jennifer Neville, Cassiano Becker +3
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on expla…
cs.LG2024
Improving Node Representation by Boosting Target-Aware Contrastive Loss
Ying-Chun Lin, Jennifer Neville
Graphs model complex relationships between entities, with nodes and edges capturing intricate connections. Node representation learning involves transforming nodes into low-dimensi…
cs.SI2024
Node Similarities under Random Projections: Limits and Pathological Cases
Tvrtko Tadić, Cassiano Becker, Jennifer Neville
Random Projections have been widely used to generate embeddings for various graph learning tasks due to their computational efficiency. The majority of applications have been justi…