229 citations · 553 across the 32 of their papers we have counts for
7 papers · 1 filter
Deconstructing equivariant representations in molecular systems
Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret
Recent equivariant models have shown significant progress in not just chemical property prediction, but as surrogates for dynamical simulations of molecules and materials. Many of…
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger, Shenyang Huang, Mikhail Galkin +9
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel model…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
Hesham Mostafa, Uday Mallappa, Mikhail Galkin +2
The floorplanning of Systems-on-a-Chip (SoCs) and of chip sub-systems is a crucial step in the physical design flow as it determines the optimal shapes and locations of the blocks…
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
Uday Mallappa, Hesham Mostafa, Mikhail Galkin +2
Floorplanning for systems-on-a-chip (SoCs) and its sub-systems is a crucial and non-trivial step of the physical design flow. It represents a difficult combinatorial optimization p…
A Foundation Model for Zero-shot Logical Query Reasoning
Mikhail Galkin, Jincheng Zhou, Bruno Ribeiro +2
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and…
Position: Graph Foundation Models are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang +6
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance the…