Latest posts tagged with #graphlearning on Bluesky
Diffusion-Assisted Distillation Enhances Graph Learning with MLPs
Diffusion-Assisted Distillation for Graph Learning with MLPs (DAD-SGM) uses a diffusion model to bridge GNN-to-MLP knowledge transfer, improving benchmarks. Code on GitHub. Read more: getnews.me/diffusion-assisted-disti... #graphlearning #mlp
Graphon Mixture‑Aware Mixup Boosts Contrastive Learning on Graph Data
Graphon‑Mixture‑Aware Mixup (GMAM) and Model‑Adaptive GCL (MGCL) boost graph learning; GMAM improves accuracy and MGCL tops average rank on eight benchmark datasets. Read more: getnews.me/graphon-mixture-aware-mi... #graphlearning #mixup
A groundbreaking algorithm blending quantum and classical methods is transforming graph representation learning, unlocking insights into complex networks like social media or biology. How will quantum computing reshape AI's future? 🤔 #QuantumComputing #GraphLearning #AIInnovation LINK
Structure-Aware Self-Supervised Learning Boosts Text-Attributed Graphs
SSTAG combines LLM‑to‑MLP and GNN‑to‑MLP distillation and uses an in‑memory repository of graph anchors for cross‑domain transfer. Read more: getnews.me/structure-aware-self-sup... #graphlearning #selfsupervised #nlp
BrainPoG: Lightweight Brain Graph Learning for Disease Detection
BrainPoG, a new brain graph learning model, achieved higher accuracy on four benchmark disease detection datasets while using fewer parameters and less processing time. Read more: getnews.me/brainpog-lightweight-bra... #brainpog #graphlearning
CueGCL Introduces Cluster-Aware Self-Training for Unsupervised Graph Learning
CueGCL uses Self‑Training (PeST) and Aligned Graph Clustering (AGC) to cut class collision. Submitted September 2025, it outperforms prior GCL methods on five benchmarks. getnews.me/cuegcl-introduces-cluste... #cuegcl #graphlearning
Not just who is connected matters — but when and in which order. ⏳ The arrow of time shapes how nodes influence each other in dynamic networks. This has big implications for graph analytics & deep learning. #AI #GraphLearning
Attentive Graph Clustering Network Enhances Transformer for Graph Data
AGCN adds attention to graph edges with a KV cache and pairwise margin contrastive loss, achieving higher silhouette and NMI scores than prior GNN and transformer models. Read more: getnews.me/attentive-graph-clusteri... #graphlearning #clustering
Contrastive Graph Learning Improves Multimodal Acoustic Classification
THGCL (Temporally Heterogeneous Graph Contrastive Learning) improves acoustic event classification, achieving higher precision on the AudioSet benchmark. Paper posted 18 Sep 2025. getnews.me/contrastive-graph-learni... #graphlearning #audioset
Graph Learning Remains Robust Under Partial Observation of Smooth Signals
Graph topology learning stays robust with smooth signals from a subset of nodes, recovering edge structure without tweaks. Preprint posted 18 Sep 2025. getnews.me/graph-learning-remains-r... #graphlearning #smoothsignals
Bayesian Sheaf Neural Networks Advance Graph Learning for Heterophilic Data
A Bayesian Sheaf Neural Network using a rotation‑group distribution via the Cayley transform achieved leading performance on heterophilic graph benchmarks. Read more: getnews.me/bayesian-sheaf-neural-ne... #bayesianns #graphlearning
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in
Heterogeneous Graphs
Weigang Lu, Wei Zhao et al.
Paper
Details
#HomophilyHeterophily #GraphLearning #RelationAwareAI
NITheCS & CoRE AI Masterclass: 'An Introduction to Graph Learning & Signal Processing'
🎓 With Dr Fei He & Stephan Goerttler (Coventry University, UK)
🗓️ Tue, 27 May 2025
🕚 11:00–13:00 SAST
📍 Join online or in person
🔗 buff.ly/6nfB5ui
#GraphLearning #SignalProcessing #AI #CoREAI #MachineLearning
📣 Call for Papers!
🧠 Neural Networks for Graphs and Beyond @ #ICANN2025
📍 Kaunas, Lithuania | 📅 Deadline: May 1, 2025
Topics: GNNs, temporal graphs, XAI, bio/brain/social data, IoT & more
🔗 Submit: e-nns.org/icann2025/su...
#GNN #GraphLearning #AI #NeuralNetworks #ICANN2025
New research from IceLab members on the #arxiv
#networkscience #graphlearning #scisky
𝗖𝗼𝗺𝗲 𝘁𝗿𝗮𝘀𝗳𝗼𝗿𝗺𝗮𝗿𝗲 𝗹𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘀𝘀𝗶𝘁𝗮̀ 𝗶𝗻 𝘃𝗮𝗹𝗼𝗿𝗲: 𝗶𝗹 𝗰𝗮𝘀𝗼 𝗚𝗥𝗔𝗙
𝗘 𝘃𝗼𝗶? 𝗖𝗼𝗺𝗲 𝘀𝘁𝗮𝘁𝗲 𝗮𝗳𝗳𝗿𝗼𝗻𝘁𝗮𝗻𝗱𝗼 𝗹𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘀𝘀𝗶𝘁𝗮̀ 𝗱𝗲𝗶 𝘃𝗼𝘀𝘁𝗿𝗶 𝗱𝗮𝘁𝗶?
#BigData #GraphLearning #Innovazione #AI #StrategiaDataDriven #DataAnalysis #MachineLearning
www.andreaviliotti.it/post/graf-un...
