Advanced Graph Neural Networks

Derniers papiers

• R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII

  Zewei Zhou, Jiajun Zou, Jiajia Zhang, Ao Yang, Ruichao He, Haozheng Zhou, Ao Liu, Jiawei Liu, Leilei Jin, Shan Shen, Daying Sun · 13 avril 2026

  Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent circuit representations and the absence of controlled evaluation protocols. We present R2G (RTL-to-GDSII), a multi-view cir…

• From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

  Hongyin Zhu, Jinming Liang, Mengjun Hou, Ruifan Tang, Xianbin Zhu, Jingyuan Yang, Yuanman Mao, Feng Wu · 13 avril 2026

  Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand -- producing decisions that are fluent but ungrounded and carrying no audit trail. …

• GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

  Ruiyao Xu, Kaize Ding · 13 avril 2026

  Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where labeled nodes are severely limited and scarce, rema…

• A Closer Look at the Application of Causal Inference in Graph Representation Learning

  Hang Gao, Kunyu Li, Huang Hong, Baoquan Cui, Fengge Wu · 13 avril 2026

  Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confounders. However, due to the inherent complexity of graph-structured data, these a…

• NOMAD: Generating Embeddings for Massive Distributed Graphs

  Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent, Ali Jannesari · 13 avril 2026

  Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preserve the graph structure. Established methods for generating embeddings require flexible exploration of the entire graph through repeated use of rando…

• LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs

  Paolo Gajo, Domenic Rosati, Hassan Sajjad, Alberto Barr\'on-Cede\~no · 13 avril 2026

  Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a promising tool for relation extraction, both in supervised and in-context learning settings. However, in this work we show…

• Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning

  Yi Luo, Xu Sun, Guangchun Luo, Aiguo Chen · 13 avril 2026

  Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer vision. However, their efficacy is severely compromised by the inherent homophily assumption, which fails to hold for heterophilic graphs where dissimilar…

• Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models

  Harry Proshian, Nikita Severin, Sergey Nikolenko, Kireev Ivan, Andrey Savchenko, Ivan Sergeev, Maria Postnova, Ilya Makarov · 13 avril 2026

  Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recommendations. While self-supervised learning (SSL) effectively models the temporal order of events, it typically overlooks …

• Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

  Andrea Schimmenti, Valentina Pasqual, Fabio Vitali, Marieke van Erp · 10 avril 2026

  Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse into structured Knowledge Graphs (KGs). This paper introduces ATR4CH (Adaptive Text-to-RDF for Cultural Heritage), a systematic five-step methodology fo…

• Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations

  Ruichen Zhang, Feda AlMuhisen, Chenguang Wan, Zhisong Qu, Kunpeng Li, Youngwoo Cho, Kyungtak Lim, Virginie Grandgirard, Xavier Garbet · 10 avril 2026

  Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leading to inefficiencies and inconsistencies. We introduce Plasma GraphRAG, a novel framework that integrates Graph Retrieval-Augmented Generation (GraphRA…

• MoE Routing Testbed: Studying Expert Specialization and Routing Behavior at Small Scale

  Tobias Falke, Nicolas Anastassacos, Samson Tan, Chankrisna Richy Meas, Chandana Satya Prakash, Nitesh Sekhar, M Saiful Bari, Krishna Kompella, Gamaleldin F. Elsayed · 9 avril 2026

  Sparse Mixture-of-Experts (MoE) architectures are increasingly popular for frontier large language models (LLM) but they introduce training challenges due to routing complexity. Fully leveraging parameters of an MoE model requires all experts to be well-trained and to specialize in non-redundant way…

• Toward a universal foundation model for graph-structured data

  Sakib Mostafa, Lei Xing, Md. Tauhidul Islam · 9 avril 2026

  Graphs are a central representation in biomedical research, capturing molecular interaction networks, gene regulatory circuits, cell--cell communication maps, and knowledge graphs. Despite their importance, currently there is not a broadly reusable foundation model available for graph analysis compa…

• BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack

  Md Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi · 9 avril 2026

  Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack for graph classification are limited to single target attack using subgraph replacement based mecha…

• k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS

  Jonas De Schouwer, Haitz S\'aez de Oc\'ariz Borde, Xiaowen Dong · 9 avril 2026

  Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modeling long-range dependencies. However, their application to large-scale graphs is hindered by the quadratic memory and computational complexity of the a…

• Energy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion

  Rui Chen, Junjun Guo, Hongbin Wang, Yan Xiang, Yantuan Xian, Zhengtao Yu · 8 avril 2026

  Graph Representation Learning (GRL) can be fundamentally modeled as a physical process of seeking an energy equilibrium state for a node system on a latent manifold. However, existing Graph Neural Networks (GNNs) often suffer from uncontrolled energy dissipation during message passing, driving the s…

• Graph Topology Information Enhanced Heterogeneous Graph Representation Learning

  He Zhao, Zhiwei Zeng, Yongwei Wang, Chunyan Miao · 8 avril 2026

  Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods have been proposed to learn graph structures and…

• MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control

  Yuchi Wang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang, Chao Feng, Hongsheng Li · 8 avril 2026

  MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reasoning into embedding learning introduces two fundamental challenges. First, structural misalignment between instance-level…

• Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

  Qimin Zhong, Hao Liao, Haiming Qin, Mingyang Zhou, Rui Mao, Wei Chen, Naipeng Chao · 8 avril 2026

  Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provid…

• OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation

  Dongying Lin, Yinan Liu, Shengwei tang, Bin Wang, Xiaochun Yang · 8 avril 2026

  Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge for most existing TKG extrapolation models is handling entities with sparse historical interaction. The ontological know…

• TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models

  Jiaquan Zhang, Qigan Sun, Chaoning Zhang, Xudong Wang, Zhenzhen Huang, Yitian Zhou, Pengcheng Zheng, Chi-lok Andy Tai, Sung-Ho Bae, Zeyu Ma, Caiyan Qin, Jinyu Guo, Yang Yang, Hengtao Shen · 8 avril 2026

  Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical applications for its single-round efficiency, yet its reasoning chains often exhibit logical gaps. While multi-round par…

• Same Graph, Different Likelihoods: Calibration of Autoregressive Graph Generators via Permutation-Equivalent Encodings

  Laurits Fredsgaard, Aaron Thomas, Michael Riis Andersen, Mikkel N. Schmidt, Mahito Sugiyama · 8 avril 2026

  Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent across all linearizations of the same graph. Segmented Eulerian Neighborhood Trails (SENT), a recent linearization method, converts graphs into s…

• k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS The Expressive Power of GraphGPS

  Jonas De Schouwer, Haitz S\'aez de Oc\'ariz Borde, Xiaowen Dong · 7 avril 2026

  Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modelling long-range dependencies. However, their application to large-scale graphs is hindered by the quadratic memory and computational complexity of the …

• Topological Sensitivity in Connectome-Constrained Neural Networks

  Nalin Dhiman · 7 avril 2026

  Connectome-constrained neural networks are often evaluated against sparse random controls and then interpreted as evidence that biological graph topology improves learning efficiency. We revisit that claim in a controlled flyvis-based study using a Drosophila connectome, a naive self-loop-matched ra…

• Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification

  Xinyan Ma (Harbin Institute of Technology, Harbin, China), Xianhao Ou (Harbin Institute of Technology, Harbin, China), Weihao Zhang (Harbin Institute of Technology, Harbin, China), Shixin Jiang (Harbin Institute of Technology, Harbin, China), Runxuan Liu (Harbin Institute of Technology, Harbin, China), Dandan Tu (Huawei Technologies Co., Ltd., Beijing, China), Lei Chen (Bay Area International Business School, Beijing Normal University, Beijing, China), Ming Liu (Harbin Institute of Technology, Harbin, China), Bing Qin (Harbin Institute of Technology, Harbin, China) · 7 avril 2026

  Knowledge Graphs (KGs) serve as a critical foundation for AI systems, yet their automated construction inevitably introduces noise, compromising data trustworthiness. Existing triple verification methods, based on graph embeddings or language models, often suffer from single-source bias by relying o…

• Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation

  Yifan Song, Fenglin Yu, Yihong Luo, Xingjian Tao, Siya Qiu, Kai Han, Jing Tang · 7 avril 2026

  Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learning systems (e.g., GNNs). Existing solutions typically rely on diffusion-based structural smoothing (e.g., feature propa…