S
Shirui Pan
Researcher at Monash University
Publications - 187
Citations - 14539
Shirui Pan is an academic researcher from Monash University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 36, co-authored 151 publications receiving 7202 citations. Previous affiliations of Shirui Pan include University of Technology, Sydney & Northwest A&F University.
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Graph Geometry Interaction Learning
TL;DR: A novel Geometry Interaction Learning method for graphs is developed, a well-suited and efficient alternative for learning abundant geometric properties in graph that captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space.
Proceedings ArticleDOI
Domain-Adversarial Graph Neural Networks for Text Classification
TL;DR: An end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification, to model documents as graphs and use a domain- adversarial training principle to lean features from each graph (as well as learning the separation of domains) for effective text classification.
Journal ArticleDOI
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
TL;DR: Suicide is a critical issue in modern society as discussed by the authors, and early detection and prevention of suicide attempts should be addressed to save people's life by early detection of suicidal ideation.
Journal ArticleDOI
Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks
TL;DR: This work presents a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs and proposes a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity.
Posted Content
Adversarially Regularized Graph Autoencoder.
TL;DR: This paper proposes a novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.