graph neural networks

Weakly-Supervised Temporal Decomposition with Graph Neural Networks for Parkinsonian Turning Assessment

Multi-scale Dual-Domain Fusion-Based Spatio-Temporal Graph Learning for fNIRS Signal Classification

Graph attention spatio-temporal graph neural network based human–light interaction modeling for indoor lighting environments

In indoor light environment design, the dynamic interaction between human behavior and lighting systems has an important impact on environmental perception results. However, existing methods are difficult to describe its spatial structure and temporal evolution characteristics at the same time. To this end, this paper...

Artificial Intelligence for Power, Performance, and Area Optimization in Digital Circuit Design

Digital circuit design becomes more difficult as electronic systems continue to increase incomplexity. Traditional Electronic Design Automation (EDA) methods use deterministic algorithmsand heuristic transformations to synthesize and optimize logic circuits, but large designs createincreasingly difficult search spaces....

Beyond Searching a Village: Learning to Recommend Diverse and Successful Collaborative Teams

Team recommendation involves selecting skilled experts to form an almost surely successful collaborative team, or refining the team composition to maintain or excel at performance. To address the tedious and error-prone manual process, computational approaches have been proposed, especially for web-scale social network...

Topological Proof Networks and Interdisciplinary Structural Isomorphisms: A Bourbaki 2.0 Framework for Categorical Graph Functor Closure and Discrete Metric Verification

Modern theoretical physics, pure mathematics, and artificial intelligence have converged upon a dual epistemological crisis: while automated neural theorem engines generate sprawling, opaque derivation steps that suffer from an unbridged "epistemic justification gap" (Tanswell & Berg, M\times\Phi 2026), human mathemati...

Messages Passed Along the Edges: A Contemporary Synthesis Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

A Citation Recommendation Algorithm Based on SciBERT and a Temporal-Aware Graph Attention Network

Citation recommendation plays a critical role in scholarly information retrieval by assisting researchers in identifying relevant and influential literature. Existing approaches typically rely on either textual semantic modeling or graph-based citation analysis, but often fail to jointly capture semantic relevance, str...

PSEALP: patch-based structural external attention with node memory for dynamic link prediction

Dynamic link prediction on temporal graphs is fundamental to many applications such as recommendation, knowledge base completion, and user–item interaction modeling. Most existing dynamic graph neural networks (DGNNs), including memory-based and attention-based models, operate on node-level embeddings and local tempora...

Manifesto of Symbiotic Homeoresis: Universal Civilizational Benchmark for Ethical AI, Salutogenesis, and Humanity Protection (Full Package & Axiomatic Core Digest)

Анотація Цей інтегрований пакет документів є єдиним суверенним депозитом, що містить повний текст та інваріантне категоріально-математичне ядро Маніфесту Симбіотичного Гомеорезу українською та англійською мовами. Праця проголошує фундаментальний гуманітарний та категоріальний зсув: Людина є єдиним суверенним Суб'єктом...

Feature-Refinement Classification Head (FCH) for Modeling Inter-Sample Relationships in Neural Networks

Conventional classification methods rely on the assumption that samples are independent and identically distributed, often ignoring the latent relational structures governing real-world data. Consequently, these methods fail to capture cross-sample dependencies, resulting in impoverished feature representations and sub...

Unbiased Recommender Systems with Implicit Feedback

Recommender systems typically rely on implicit feedback (e.g., clicks) to infer user preferences. However, such data is inherently prone to various biases, including position bias and popularity bias. Position bias occurs when higher-ranked items receive more interactions regardless of true relevance. Popularity bias r...