Search Results for author: Kuansan Wang

Found 19 papers, 12 papers with code

SciConceptMiner: A system for large-scale scientific concept discovery

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).

MATCH: Metadata-Aware Text Classification in A Large Hierarchy

1 code implementation15 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.

General Classification Multi Label Text Classification +2

Generalizing Graph Convolutional Networks

1 code implementation1 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.

Explainable and Sparse Representations of Academic Articles for Knowledge Exploration

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.

GPT-GNN: Generative Pre-Training of Graph Neural Networks

3 code implementations27 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.

Graph Generation

Heterogeneous Graph Transformer

4 code implementations3 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.

Graph Sampling Node Property Prediction

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

1 code implementation26 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.

Network Embedding

A Scalable Hybrid Research Paper Recommender System for Microsoft Academic

1 code implementation21 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.

Recommendation Systems

DeepInf: Social Influence Prediction with Deep Learning

1 code implementation15 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.

Feature Engineering Representation Learning

A Web-scale system for scientific knowledge exploration

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.

Efficient Exploration TAG

Revisiting Knowledge Base Embedding as Tensor Decomposition

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.

Link Prediction Tensor Decomposition

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

4 code implementations9 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.

Network Embedding

A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

no code implementations17 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

An Overview of Microsoft Academic Service (MAS) and Applications

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.

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