257 citations · 1.2k across the 74 of their papers we have counts for
9 papers · 1 filter
Leveraging the Graph Structure of Neural Network Training Dynamics
Fatemeh Vahedian, Ruiyu Li, Puja Trivedi +2
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance. Recent works have demonstrated…
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan +2
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure. Contrastive learning (CL) is an increasin…
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
Di Jin, Bunyamin Sisman, Hao Wei +2
Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact…
How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications
Jiong Zhu, Junchen Jin, Donald Loveland +2
We bridge two research directions on graph neural networks (GNNs), by formalizing the relation between heterophily of node labels (i.e., connected nodes tend to have dissimilar lab…
Relational World Knowledge Representation in Contextual Language Models: A Review
Tara Safavi, Danai Koutra
Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines. However, while advantageous for their high degree of precision and interpretability, KB…
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
Jing Zhu, Xingyu Lu, Mark Heimann +1
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node ro…