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ORCIDPKDD / ECML 2023: Turin, Italy - Part III
- Danai Koutra
, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part III. Lecture Notes in Computer Science 14171, Springer 2023, ISBN 978-3-031-43417-4
Graph Neural Networks
- Lirong Wu, Cheng Tan, Zihan Liu, Zhangyang Gao, Haitao Lin, Stan Z. Li:
Learning to Augment Graph Structure for both Homophily and Heterophily Graphs. 3-18 - Akshay Sethi, Sonia Gupta, Aakarsh Malhotra, Siddhartha Asthana:
Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learning. 19-35 - Piotr Gainski, Michal Koziarski, Jacek Tabor, Marek Smieja:
ChiENN: Embracing Molecular Chirality with Graph Neural Networks. 36-52 - Wenlan Kuang, Qiangxi Zhu, Zhixin Li:
Multi-label Image Classification with Multi-scale Global-Local Semantic Graph Network. 53-69 - Guixiang Cheng, Xin Yan, Shengxiang Gao, Guangyi Xu, Xianghua Miao:
CasSampling: Exploring Efficient Cascade Graph Learning for Popularity Prediction. 70-86 - Shaowei Wei, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou:
Boosting Adaptive Graph Augmented MLPs via Customized Knowledge Distillation. 87-103 - Yucheng Shi, Kaixiong Zhou, Ninghao Liu:
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning. 104-121 - Wei Zhao, Federico López, J. Maxwell Riestenberg, Michael Strube, Diaaeldin Taha, Steve Trettel:
Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices. 122-139 - Xugang Wu, Huijun Wu, Ruibo Wang, Duanyu Li, Xu Zhou, Kai Lu:
Leveraging Free Labels to Power up Heterophilic Graph Learning in Weakly-Supervised Settings: An Empirical Study. 140-156 - Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, George Karypis:
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs. 157-173
Graphs
- Maximilian Stubbemann, Gerd Stumme:
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. 177-192 - Zuowu Zheng, Chao Wang, Xiaofeng Gao, Guihai Chen:
RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure. 193-208 - Yuting Liang, Wen Bai, Yuncheng Jiang:
A Unified Spectral Rotation Framework Using a Fused Similarity Graph. 209-225 - Liping Yan, Weiren Yu:
SimSky: An Accuracy-Aware Algorithm for Single-Source SimRank Search. 226-241 - Laura Toni, Pascal Frossard:
Online Network Source Optimization with Graph-Kernel MAB. 242-258 - Jakir Hossain, Sucheta Soundarajan, Ahmet Erdem Sariyüce:
Quantifying Node-Based Core Resilience. 259-276 - Adrian Horzyk, Daniel Bulanda, Janusz A. Starzyk:
Construction and Training of Multi-Associative Graph Networks. 277-292 - Danylo Honcharov, Ahmet Erdem Sariyüce, Ricky Laishram, Sucheta Soundarajan:
Skeletal Cores and Graph Resilience. 293-308 - Abdullah Alchihabi, Yuhong Guo:
GDM: Dual Mixup for Graph Classification with Limited Supervision. 309-324 - Bosong Huang, Weihao Yu, Ruzhong Xie, Jing Xiao, Jin Huang:
Two-Stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems. 325-340
Interpretability
- Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett:
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity. 343-359 - Kentaro Kanamori:
Learning Locally Interpretable Rule Ensemble. 360-377 - Brigt Arve Toppe Håvardstun, Cèsar Ferri, José Hernández-Orallo, Pekka Parviainen, Jan Arne Telle:
XAI with Machine Teaching When Humans Are (Not) Informed About the Irrelevant Features. 378-393 - Victor Guyomard, Françoise Fessant, Thomas Guyet, Tassadit Bouadi, Alexandre Termier:
Generating Robust Counterfactual Explanations. 394-409 - Tathagata Raha, Mukund Choudhary, Abhinav Menon, Harshit Gupta, KV Aditya Srivatsa, Manish Gupta, Vasudeva Varma:
Neural Models for Factual Inconsistency Classification with Explanations. 410-427 - Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. 428-445 - Fereshteh Razmi, Jian Lou, Yuan Hong, Li Xiong:
Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records. 446-461 - Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke:
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. 462-478 - Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. 479-495 - Milan Bhan, Jean-Noël Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot:
TIGTEC: Token Importance Guided TExt Counterfactuals. 496-512
Knowledge Graphs
- Zicheng Zhao, Linhao Luo, Shirui Pan, Quoc Viet Hung Nguyen, Chen Gong:
Towards Few-Shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-Guided Neural Process Approach. 515-532 - Julia Gastinger, Timo Sztyler, Lokesh Sharma, Anett Schuelke, Heiner Stuckenschmidt:
Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting. 533-549 - Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, Volker Tresp:
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs Using Confidence-Augmented Reinforcement Learning. 550-566 - Caglar Demir, Axel-Cyrille Ngonga Ngomo:
Clifford Embeddings - A Generalized Approach for Embedding in Normed Algebras. 567-582 - Zhaobo Zhang, Pingpeng Yuan, Hai Jin:
Exploring Word-Sememe Graph-Centric Chinese Antonym Detection. 583-600 - Bowen Song, Chengjin Xu, Kossi Amouzouvi, Maocai Wang, Jens Lehmann, Sahar Vahdati:
Distinct Geometrical Representations for Temporal and Relational Structures in Knowledge Graphs. 601-616 - Caglar Demir, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo, Stefan Heindorf:
LitCQD: Multi-hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals. 617-633
Large-Scale Learning
- An Xu, Yang Bai:
Cross Model Parallelism for Faster Bidirectional Training of Large Convolutional Neural Networks. 637-653 - An Xu, Yang Bai:
Distributed Adaptive Optimization with Divisible Communication. 654-670 - Giambattista Amati, Antonio Cruciani, Daniele Pasquini, Paola Vocca, Simone Angelini:
propagate: A Seed Propagation Framework to Compute Distance-Based Metrics on Very Large Graphs. 671-688 - Erik Schultheis, Rohit Babbar:
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU. 689-704
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