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E-raamat: Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part III

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This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.

The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Pharmaceutical Data Analysis.- Drug-target interaction prediction based
on drug subgraph fingerprint extraction strategy and subgraph attention
mechanism.- Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR
Understanding.- Graph Convolution Synthetic Transformer for Chronic Kidney
Disease Onset Prediction.- MTFL: Multi-task feature learning with joint
correlation structure learning for Alzheimers disease cognitive performance
prediction.- Multi-Level Transformer for Cancer Outcome Prediction in
Large-Scale Claims Data.- Individual Functional Network Abnormalities Mapping
via Graph Representation-based Neural Architecture Search.- A novel
application of a mutual information measure for analysing temporal changes in
healthcare network graphs.- Drugs Resistance Analysis from Scarce Health
Records via Multi-task Graph Representation.- Text
Classification.- ParaNet:Parallel Networks with Pre-trained Models for Text
Classification.- Open Text Classification Based on Dynamic Boundary
Balance.- A Prompt Tuning Method for Chinese Medical Text
Classification.- TabMentor: Detect Errors on Tabular Data with Noisy
Labels.- Label-aware Hierarchical Contrastive Domain Adaptation for
Cross-network Node Classification.- Semi-supervised classification based on
Graph Convolution Encoder Representations from BERT.- Global Balanced Text
Classification for Stable Disease Diagnosis.- Graph.- Dominance Maximization
in Uncertain Graphs.- LAGCL: Towards Stable and Automated Graph Contrastive
Learning.- Discriminative Graph-level Anomaly Detection via
Dual-students-teacher Model.- Common-Truss-based Community Search on
Multilayer Graphs.- Learning To Predict Shortest Path Distance.- Efficient
Regular Path Query Evaluation with Structural Path Constraints.EnSpeciVAT:
Enhanced SpeciVAT for Cluster Tendency Identification in Graphs.- Pessimistic
Adversarially Regularized Learning for Graph Embedding.- M2HGCL: Multi-Scale
Meta-Path Integrated Heterogeneous Graph Contrastive Learning.
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