17 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
Bridging Input Feature Spaces Towards Graph Foundation Models
Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua +2
Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but also in value ranges and di…
Towards Improved Sentence Representations using Token Graphs
Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Zorah Lähner +1
Obtaining a single-vector representation from a Large Language Model's (LLM) token-level outputs is a critical step for nearly all sentence-level tasks. However, standard pooling m…
Revisiting Node Affinity Prediction in Temporal Graphs
Or Feldman, Krishna Sri Ipsit Mantri, Moshe Eliasof +1
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent w…
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2
Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Henc…
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb +3
In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible ac…