56 citations · 71 across the 20 of their papers we have counts for
5 papers · 1 filter
A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights
Jaewan Chun, Fanchen Bu, Yeongho Kim +3
Identifying central entities and interactions is a fundamental problem in network science. While well-studied for graphs (pairwise relations), many biological and social systems ex…
PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs
Fanchen Bu, Kijung Shin
Geometric learning has emerged as a powerful paradigm for modeling non-Euclidean data, especially graph-structured ones, with applications spanning social networks, molecular struc…
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and Analysis
Fanchen Bu, Kijung Shin
In unsupervised combinatorial optimization (UCO), during training, one aims to have continuous decisions that are promising in a probabilistic sense for each training instance, whi…
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
Langzhang Liang, Fanchen Bu, Zixing Song +3
The message-passing paradigm of Graph Neural Networks often struggles with exchanging information across distant nodes typically due to structural bottlenecks in certain graph regi…
'Hello, World!': Making GNNs Talk with LLMs
Sunwoo Kim, Soo Yong Lee, Jaemin Yoo +1
While graph neural networks (GNNs) have shown remarkable performance across diverse graph-related tasks, their high-dimensional hidden representations render them black boxes. In t…