graph-neural-network

EnergyMamba proposes a novel spatiotemporal framework combining a graph-enhanced selective state space model and adaptive conformalized quantile regression for accurate and reliable energy consumption prediction with uncertainty estimates, achieving improvements on real-world datasets from Florida, New York, and California.

KG-Guard is a lightweight graph-based framework for detecting hallucinations in LLM-based knowledge base question answering. It treats the LLM as a black box and uses a graph encoder with a MLP classifier to identify hallucinated answer nodes, outperforming baselines while having far fewer parameters.

This paper presents a graph-learning-aided optimization approach for designing active tether-net systems to capture space debris, using a GNN to recommend candidate designs and reduce mixed-combinatorial nonlinear programming to standard NLP problems, achieving faster convergence.

GiG is a knowledge graph-modulated deep learning framework that integrates biological knowledge graphs as edges and patient-specific data as node features, outperforming SOTA by up to 49% in limited-sample clinical tasks.

This paper introduces Graph Alignment Topology as an inductive bias for grounding detection, using a graph neural network to model alignment structure between reference information and LLM outputs. The method achieves state-of-the-art results on multiple hallucination and question-answering datasets, outperforming GPT-4o.

This paper identifies three failure modes in existing antibody design methods (antigen blindness, vocabulary collapse, convergence to marginal distribution) and proposes AgForce, a novel encoder-decoder architecture using graph neural networks and mixture density networks, achieving state-of-the-art binding quality and sequence recovery on the Chimera-Bench benchmark.

ConTact introduces a contact-then-act architecture for antibody CDR design that explicitly decomposes the task into interface reasoning, contact prediction, and contact-gated sequence generation, achieving state-of-the-art structural quality and epitope awareness on the Chimera-Bench benchmark.

This paper proposes a graph-driven real-time anti-money laundering monitoring framework (GCRMF) for cross-industry supply chain networks, leveraging heterogeneous graphs and temporal attention networks, achieving over 17.8% F1 improvement.

Proposes MacroDiff+, a physics-guided geometric diffusion framework for macro placement in VLSI design, achieving 6.1–6.2% wirelength reduction on ISPD2005 benchmarks with superior stability and scalability.

This paper proposes a graph-based one-stage framework for brain tumor segmentation that handles missing MRI modalities by introducing modality-specific virtual nodes and a dynamic connection strategy, outperforming state-of-the-art methods on the BRATS-2018 and BRATS-2020 datasets.

Njord is a probabilistic graph neural network for ensemble ocean forecasting that provides uncertainty estimates and achieves state-of-the-art performance on global and regional benchmarks, improving surface temperature prediction.

Microsoft open-sourced GridSFM, an AI foundation model for power systems. It uses graph neural networks to approximate AC-OPF solving, is topology-agnostic, and can serve as a warm start for exact solvers achieving a 1.45x speedup, while also providing feasibility classification capabilities.

This paper proposes a graph neural network framework for financial fraud detection that integrates transaction records and identity information into node attributes, employs a multi-layer message passing mechanism, and uses weighted supervision and structural consistency regularization to improve risk scoring and probability calibration. Experiments on a public dataset show the method outperforms existing approaches.