Showing 17 papers for 2026-02-27
We developed a passive surveillance system for early stroke risk detection in high-risk individuals with diabetes, using patient-reported symptoms. A symptom taxonomy grounded in patients' own language and a dual ML pipeline combining heterogeneous GNNs and EN/LASSO identified patterns predictive of stroke, leading to a hybrid risk screening tool.
We propose a self-supervised heterogeneous graph neural network to improve spatial allocation for energy system coupling with mismatched spatial resolutions. It models high-resolution geographic units as graph nodes and fuses multiple geographical features to produce physically meaningful aggregation weights across scales.
ECHO introduces high-order operators to encode communities in attributed networks, offering a scalable, self-supervised solution that overcomes the semantic walls of feature over-smoothing in dense or heterophilic networks and the systems wall from O(N^2) memory. It reframes community detection to scale with data size.
DyGnROLE proposes a transformer-based dynamic graph model that explicitly separates source and destination representations to capture asymmetry. It uses distinct embedding vocabularies and role-aware semantics to model temporal dynamics.
We develop deep ensemble graph neural nets to reconstruct cosmic-ray arrival direction and energy from voltage traces on ground-based radio detectors. Representing triggered antennas as a graph, the GNN leverages physical knowledge to improve accuracy and reduce training data requirements.
This work presents a physics-inspired neural framework for large-scale graph coloring, combining graph neural networks with statistical-mechanics ideas and planting-based supervision to address the algorithmic phase-transition challenges.
Atlas-free Brain Network Transformer proposes an atlas-free approach to constructing brain networks, avoiding fixed anatomical atlases that cause misalignment and biases. It introduces a transformer-based method to learn brain connectivity directly from data.
GYWI combines author knowledge graphs with retrieval-augmented generation to provide controllable context and traceable inspiration paths for LLM-based scientific idea generation. The system centers on building an author-centric knowledge graph to guide generation.
The paper analyzes retriever-reranker pipelines for RAG over knowledge graphs in e-commerce, comparing how to scale retrieval across connected graphs and preserve graph structure. It discusses challenges and benchmarks for KG-based RAG in practical applications.
Contextual Memory Virtualisation (CMV) treats accumulated LLM understanding as version-controlled state via a DAG-based memory model, enabling structured state management and lossless trimming of history for long-running agent sessions.
This work bridges granularity mismatch between LLMs and knowledge graphs by addressing token-level vs entity-level representations, proposing methods to better align semantic text with graph structure for knowledge graph completion.
TCM-DiffRAG develops a personalized syndrome differentiation reasoning method for Traditional Chinese Medicine using knowledge graphs and chain-of-thought prompting to tackle diverse diagnostic patterns and individual differences.
G-reasoner presents foundation models for unified reasoning over graph-structured knowledge, addressing fragmented information in RAG by integrating graph-structured data into reasoning with LLMs.
MAGNET proposes Modality-Guided Mixture of Adaptive Graph Experts with entropy-triggered routing to fuse heterogeneous multimodal signals for recommendation, addressing modality imbalance and entangled representations.
PoSh introduces a detailed image description metric that uses scene graphs as structured rubrics to guide LLMs-as-a-judge, enabling fine-grained attribution and relation-aware evaluation.
AlayaLaser tackles I/O-bound misperception in on-disk graph-based vector search by proposing an optimized index layout and search strategy that focuses on compute efficiency for high-dimensional vectors.
VeloANN improves SSD-resident graph indexing for high-throughput vector search by introducing a locality-aware data layout and coroutine-based asynchronous processing to reduce storage stalls and improve CPU utilization.