1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
GNN-CB: A Graph Neural Network Competition Benchmark for Human and LLM Evaluation
Murad Hossen, Tasneem Selim, Gurur Gamgam +22
Large language models (LLMs) have demonstrated strong performance on coding and reasoning benchmarks; however, their ability to solve graph-structured machine learning problems rem…
Reservoir-Based Graph Convolutional Networks
Mayssa Soussia, Gita Ayu Salsabila, Mohamed Ali Mahjoub +1
Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convo…
Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelity
Pragya Singh, Islem Rekik
Graph super-resolution, the task of inferring high-resolution (HR) graphs from low-resolution (LR) counterparts, is an underexplored yet crucial research direction that circumvents…
DeltaGNN: Graph Neural Network with Information Flow Control
Kevin Mancini, Islem Rekik
Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process.…
DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
K. Mancini, I. Rekik
Graph Neural Networks (GNNs) have proven effective in various medical imaging applications, such as automated disease diagnosis. However, due to the local neighborhood aggregation…