Da Zhang

Orcid: 0000-0003-3321-1835

Affiliations:
  • University of Miami, Coral Gables, FL, USA


According to our database1, Da Zhang authored at least 16 papers between 2017 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2021
Distributed Relationship Mining over Big Scholar Data.
IEEE Trans. Emerg. Top. Comput., 2021

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021

2020
The Gene of Scientific Success.
ACM Trans. Knowl. Discov. Data, 2020

Ranking Station Importance With Human Mobility Patterns Using Subway Network Datasets.
IEEE Trans. Intell. Transp. Syst., 2020

Motif discovery in networks: A survey.
Comput. Sci. Rev., 2020

Network embedding: Taxonomies, frameworks and applications.
Comput. Sci. Rev., 2020

Big networks: A survey.
Comput. Sci. Rev., 2020

2019
Data Mining and Information Retrieval in the 21st century: A bibliographic review.
Comput. Sci. Rev., 2019

Multimodal deep representation learning for protein interaction identification and protein family classification.
BMC Bioinform., 2019

A Survey of Measures for Network Motifs.
IEEE Access, 2019

2018
Protein Family Classification with Multi-Layer Graph Convolutional Networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

Multimodal Deep Representation Learning for Protein-Protein Interaction Networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

2017
Top-K Entity Units Retrieval Over Big Data.
Proceedings of the 26th International Conference on World Wide Web Companion, 2017

Parallelization of Conjunctive Query Answering over Ontologies.
Proceedings of the Information Management and Big Data, 2017

Parallelization of Query Processing over Expressive Ontologies.
Proceedings of the 4th Annual International Symposium on Information Management and Big Data, 2017

Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach.
Proceedings of the 15th IEEE Intl Conf on Dependable, 2017


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