ORCID as entered in ROS

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2024, SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation, http://dx.doi.org/10.48550/arxiv.2405.09939
,2024, Deep Overlapping Community Search via Subspace Embedding, http://dx.doi.org/10.48550/arxiv.2404.14692
,2024, Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model, http://dx.doi.org/10.48550/arxiv.2404.03080
,2024, Inferring gene regulatory networks by hypergraph variational autoencoder, http://dx.doi.org/10.1101/2024.04.01.586509
,2023, Batch Hop-Constrained s-t Simple Path Query Processing in Large Graphs, http://dx.doi.org/10.48550/arxiv.2312.01424
,2023, DARWIN Series: Domain Specific Large Language Models for Natural Science, http://dx.doi.org/10.48550/arxiv.2308.13565
,2023, Efficient Non-Learning Similar Subtrajectory Search, http://dx.doi.org/10.48550/arxiv.2307.10082
,2023, Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT, http://dx.doi.org/10.48550/arxiv.2304.02213
,2023, HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs, http://arxiv.org/abs/2302.06119v2
,2022, Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance, http://dx.doi.org/10.36227/techrxiv.21203096
,2022, Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance, http://dx.doi.org/10.36227/techrxiv.21203096.v1
,2022, GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs, http://dx.doi.org/10.48550/arxiv.2208.06144
,2022, Balanced Clique Computation in Signed Networks: Concepts and Algorithms, http://dx.doi.org/10.48550/arxiv.2204.00515
,2021, Towards User Engagement Dynamics in Social Networks, http://dx.doi.org/10.48550/arxiv.2110.12193
,2021, Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model, http://dx.doi.org/10.48550/arxiv.2109.02058
,2021, HUGE: An Efficient and Scalable Subgraph Enumeration System, http://arxiv.org/abs/2103.14294v2
,2020, Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation, http://dx.doi.org/10.48550/arxiv.2004.08068
,2019, A Survey and Experimental Analysis of Distributed Subgraph Matching, http://dx.doi.org/10.48550/arxiv.1906.11518
,2017, Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval, http://dx.doi.org/10.48550/arxiv.1701.05003
,Efficient Continuous kNN Join over Dynamic High-dimensional Data [v1], http://dx.doi.org/10.21203/rs.3.rs-2572561/v1
,Efficient Continuous kNN Join over Dynamic High-dimensional Data [v2], http://dx.doi.org/10.21203/rs.3.rs-2572561/v2
,Efficient Continuous kNN Join over Dynamic High-dimensional Data [v3], http://dx.doi.org/10.21203/rs.3.rs-2572561/v3
,Efficient Continuous kNN Join over Dynamic High-dimensional Data [v4], http://dx.doi.org/10.21203/rs.3.rs-2572561/v4
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