Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VII(15876 Lecture Notes in Computer Science)

About the Book

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.

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.