Overview

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

Full Product Details

Author:   Xintao Wu ,  Myra Spiliopoulou ,  Can Wang ,  Vipin Kumar
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Volume:   15876
ISBN:  

9789819682973

ISBN 10:   9819682975
Pages:   441
Publication Date:   15 June 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available  
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Table of Contents

.- Graph Mining. .- MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion. .- Tensor-Fused Multi-View Graph Contrastive Learning. .- FOG: Interpretable Feature-Oriented Graph Neural Networks for Tabular Data  Prediction. .- High Resolution Image Classification with Rich Text Information Based on Graph Convolution Neural Network. .- Time Interval Aware Graph Neural Networks for Session-Based Recommendation. .- SSGNN: Structure-aware Scoring Graph Neural Network for Molecular Representation. .- Mint: An Efficient and Robust In-Place Update Approach for Graph-based Vector Index. .- Machine Learning Applications. .- Advancing Comprehensive Aspect-Based Sentiment Analysis with Generative Models. .- A Systematic Evaluation of Generative Models on Tabular Transportation Data. .- SDF-Guided Multi-modal Big Data Road Extraction. .- Player Movement Predictions Using Team and Opponent Dynamics for Doubles Badminton. .- Representation Learning. .- Late Fusion Ensembles for Speech Recognition on Diverse Input Audio Representations. .- Text Enhancement-based Multimodal Fusion for Video Sentiment Analysis. .- Advancing Rubric-based Automated Essay Scoring with Multi-View BERT: A Case Study in New Zealand. .- A Script Event Prediction Method Based on Multi-Level Joint Pretraining and Prompt Fine-Tuning. .- Scientific/Business Data Analysis. .- A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection. .- Using Pseudo-Synonyms to Generate Embeddings for Clinical Terms. .- Corporate Carbon Emission Prediction: Combining Structured and Unstructured Data. .- GDCK: Efficient Large-Scale Graph Distillation utilizing a Model-free Kernelized Approach. .- Efficient DNA fragment assembly based on Discrete Slime Mould Algorithm. .- Multi-Scale Control Model for Network Group Behavior. .- Can Self Supervision Rejuvenate Similarity-Based Link Prediction?. .- Managing Data Uncertainty in Automatic Mapping of Clinical Classification Systems. .- Insomnia Detection Based on Brain State Sleep Trajectories. .- MCA: Multimodal Contrastive Augmentation for Medical Report Generation. .- Special Track on Large Language Models. .-Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection. .- Bot Wars Evolved: Orchestrating Competing LLMs in a Counterstrike Against Phone Scams. .- Large Language Models with Multi-Faceted Relation Alignment for User Novel Interest Discovery. .- Estimating Impact of Behavior Change Messages Using Large Language Models. .- A Meta-Thinking Approach to Mitigating Linguistic Sycophancy in Vision-Language Models. .- VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models. .- TRAWL: Tensor Reduced and Approximated Weights for Large Language Models. .- DAG-Think-Twice: Causal Structure Guided Elicitation of Causal Reasoning in Large Language Model. .- GRL-Prompt: Towards Prompts Optimization via Graph-empowered Reinforcement Learning using LLMs’ Feedback.

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