no code implementations • NAACL (sdp) 2021 • Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Wang
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
1 code implementation • 11 Feb 2022 • Yu Zhang, Zhihong Shen, Chieh-Han Wu, Boya Xie, Junheng Hao, Ye-Yi Wang, Kuansan Wang, Jiawei Han
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
no code implementations • ACL 2021 • Zhihong Shen, Chieh-Han Wu, Li Ma, Chien-Pang Chen, Kuansan Wang
In this paper, we introduce a self-supervised end-to-end system, SciConceptMiner, for the automatic capture of emerging scientific concepts from both independent knowledge sources (semi-structured data) and academic publications (unstructured documents).
1 code implementation • 15 Feb 2021 • Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.
1 code implementation • 1 Jan 2021 • Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, Kuansan Wang
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
no code implementations • COLING 2020 • Keng-Te Liao, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, PoChun Chen, Kuansan Wang, Shou-De Lin
Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications.
3 code implementations • 27 Jun 2020 • Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
4 code implementations • 17 Jun 2020 • Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
4 code implementations • ACL 2020 • Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex Wade, Kuansan Wang, Nancy Xin Ru Wang, Chris Wilhelm, Boya Xie, Douglas Raymond, Daniel S. Weld, Oren Etzioni, Sebastian Kohlmeier
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research.
4 code implementations • 3 Mar 2020 • Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Ranked #17 on Node Property Prediction on ogbn-mag
2 code implementations • 26 Jan 2020 • Jiaming Shen, Zhihong Shen, Chenyan Xiong, Chi Wang, Kuansan Wang, Jiawei Han
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications.
1 code implementation • 26 Jun 2019 • Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang
Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.
1 code implementation • 21 May 2019 • Anshul Kanakia, Zhihong Shen, Darrin Eide, Kuansan Wang
We present the design and methodology for the large scale hybrid paper recommender system used by Microsoft Academic.
1 code implementation • 15 Jul 2018 • Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.
no code implementations • ACL 2018 • Zhihong Shen, Hao Ma, Kuansan Wang
To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure.
no code implementations • ICLR 2018 • Jiezhong Qiu, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.
4 code implementations • 9 Oct 2017 • Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
no code implementations • 17 Apr 2017 • Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang
We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s.
Digital Libraries Social and Information Networks Physics and Society
no code implementations • WWW 2015 • Arnab Sinha, Zhihong Shen, Yang song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, Kuansan Wang
In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine.