5 accepted papers
Large Language Model (LLM) agents have demonstrated remarkable proficiency in solving isolated software engineering tasks. However, existing benchmarks predominantly evaluate static, independent issues, failing to reflect the continuous and sequentially dependent nature of real-world software evolut…
Uncertainty quantification is essential for reliable federated graph learning, yet existing methods struggle with decentralized and heterogeneous data. In this work, we first extend Conformal Prediction (CP), a well-established method for uncertainty quantification, to federated graph learning, form
Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph neural networks (GNNs). Increasing the GNN depth can expand the scope (i.e., receptive field), potentially finding homophily from the higher-order neighborhoods. However, GNNs suffer from performance degradation…
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i.e., receptive field). Beyond just a few layers, two fundamental challenges emerge: 1. degra…
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a g…