Results 251 to 260 of about 1,402,234 (289)

Leveraging the citation graph to recommend keywords

Proceedings of the 7th ACM conference on Recommender systems, 2013
Users of scientific papers databases, such as CiteSeer, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers.
Ido Blank, Lior Rokach, Guy Shani
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Graph Neural Collaborative Topic Model for Citation Recommendation

ACM Transactions on Information Systems, 2021
Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations.
Qianqian Xie, Yutao Zhu 0001, Jimin Huang, Pan Du 0001, Jian-Yun Nie   +4 more
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A Graph Clustering Algorithm for Citation Networks

2016
In this paper, we propose a novel network clustering algorithm, called CPSCAN for detecting the communities of citation network based on the temporal feature. Firstly, with combining temporal interval and citation path, a structural similarity model and a clustering algorithm is proposed.
Bo Zhang, Tiezheng Nie, Derong Shen, Yue Kou, Ge Yu 0001, Ziwei Zhou   +5 more
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Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?

International Conference on Learning Representations
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data. Hypergraphs, which can model complex beyond-pairwise relationships, offer
Yifan Feng, Chengwu Yang, Xingliang Hou, Shaoyi Du, Shihui Ying, Zongze Wu, Yue Gao   +6 more
semanticscholar   +1 more source

Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

ACM Transactions on Intelligent Systems and Technology
The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles.
Jhih-Chen Liu, Chiao-Ting Chen, Chi Lee, Szu-Hao Huang   +3 more
semanticscholar   +1 more source

When Transformer Meets Large Graphs: An Expressive and Efficient Two-View Architecture

IEEE Transactions on Knowledge and Data Engineering
The successes of applying Transformer to graphs have been witnessed on small graphs (e.g., molecular graphs), yet two barriers prevent its adoption on large graphs (e.g., citation networks).
Weirui Kuang, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding   +4 more
semanticscholar   +1 more source

TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs

Neural Information Processing Systems
Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings.
Zhuofeng Li, Zixing Gou, Xiangnan Zhang, Zhongyuan Liu, Sirui Li, Yuntong Hu, Chen Ling, Zhengwu Zhang, Liang Zhao   +8 more
semanticscholar   +1 more source

ReViz: A Tool for Automatically Generating Citation Graphs and Variants

2020
A systematic literature review provides an overview of multiple scientific publications in an area of research and visualizations of the data of the systematic review enable further in-depth analyses. The creation of such a review and its visualizations is a very time- and labor-intensive process.
Sven Groppe, Lina Hartung
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On Understanding Centrality in Directed Citation Graph

2014
Modeling complex networks as directed/undirected graphs is considered one of the most common methods in network science. Citation graph is a directed graph of scientific published papers. This graph has been studied massively in the past decade. Citation graph can be utilized to study relationships between authors and papers.
Ismael A. Jannoud, Mohammad Z. Masoud
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VEGAS: Visual influEnce GrAph Summarization on Citation Networks

IEEE Transactions on Knowledge and Data Engineering, 2015
Visually analyzing citation networks poses challenges to many fields of the data mining research. How can we summarize a large citation graph according to the user’s interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link graph structure while revealing the
Lei Shi 0002, Hanghang Tong, Jie Tang 0001, Chuang Lin 0002   +3 more
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