- [Aug, 2024] Our paper “S3PaR: Section-Based Sequential Scientific Paper Recommendation for Paper Writing Assistance” was accepted for publication in Knowledge-Based Systems
- [May, 2024] Our paper “How Powerful is Graph Filtering for Recommendation” was accepted to KDD’2024
- [Oct, 2023] Our paper “Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph” was accepted for publication in MLJ
- [Oct, 2023] Our paper “Class-Incremental Learning Using Diffusion Model for Distillation and Replay” won the best paper award at ICCV’23 Workshop on Visual Continual Learning. Congratulations Jodelet!
- [Sep, 2023] Our paper “Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation” was accepted for publication in Neurocomputing
- [Jul, 2023] Our paper “MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning” was accepted to ACM MM’2023
- [Jul, 2023] Our paper “What Wikipedia Misses about Yuriko Nakamura? Predicting Missing Biography Content by Learning Latent Life Patterns” was accepted to ECAI’2023
- [Dec, 2022] Our paper “Feature Selection: Key to Enhance Node Classification with Graph Neural Networks” was accepted for publication in CAAI TIT
- [Dec, 2022] Our paper “DS-Net: A Dedicated Approach for Collapsed Building Detection from Post-Event Airborne Point Clouds” was accepted for publication in IJAEOG
- [Dec, 2022] Mr.$\,$Tai Hasegawa joined us as a research assistant
- [Oct, 2022] Our paper “Balanced Softmax Cross-Entropy for Incremental Learning with and without Memory” was accepted for publication in CVIU
- [Aug, 2022] Our paper “Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification” was accepted to CIKM’22 (short paper)
- [Jun, 2022] Our paper “Anonymity Can Help Minority: A Novel Synthetic Data Over-Sampling Strategy on Multi-Label Graphs” was accepted to ECML-PKDD’2022
- [Apr, 2022] Our paper “Simplifying Approach to Node Classification in Graph Neural Networks” was accepted for publication in Journal of Computational Science
- [Feb, 2022] Our paper “Heterogeneous Graph Embedding with Single-Level Aggregation and Infomax Encoding” was accepted for publication in MLJ
- [Dec, 2021] Our paper “Leaping Through Time with Gradient-based Adaptation for Recommendation” was accepted to AAAI’22
- [Nov, 2021] Ms.$\,$Natasha Santosa joined us as a research assistant
- [Oct, 2021] Our paper “Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention” was accepted to BMVC’21
- [Aug, 2021] Our paper “Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction” was accepted to CIKM’21
- [Aug, 2021] Our paper “Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph” was accepted to EMNLP’21 (Findings)
- [Apr, 2021] Our paper “PGRA: Projected Graph Relation-Feature Attention Network for Heterogeneous Information Network Embedding” was accepted for publication in Information Sciences
- [Dec, 2020] Our paper “Graph Neural Networks for Fast Node Ranking Approximation” was accepted for publication in TKDD
- [Nov, 2020] Our paper “Graph Convolutional Networks for Graphs Containing Missing Features” was accepted for publication in FGCS
- [Oct, 2020] Our paper “Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Convolution Network” was accepted to ICDE’21
- [Jul, 2020] Our paper “Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision” was accepted to CIKM’20
- [Aug, 2019] Our paper “Fast Approximations of Betweenness Centrality with Graph Nural Networks” was accepted to CIKM’19 (short paper)
- [Oct, 2018] Our paper “A General View for Network Embedding as Matrix Factorization” was accepted to WSDM’19
- [Aug, 2018] Our paper “Learning Community Structure with Variational Autoencoder” was accepted to ICDM’18
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