Proceedings ArticleDOI
Topic Influence Graph Based Analysis of Seminal Papers
Abhirut Gupta,Sandipan Sikdar,Prateeti Mohapatra,Niloy Ganguly +3 more
- pp 224-228
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TLDR
This paper construct the TIG on the ACL Anthology Network dataset and leverage it to analyze the properties of seminal papers and observe that seminal papers disseminate knowledge across different communities, trigger more research within its own community and apart from introducing new ideas, string together ideas from different communities.Abstract:
Every scientific article attempts to derive knowledge from existing literature and augment it with new insights. This dynamics of knowledge is commonly explored through references (it draws knowledge from) and citations (its impact on the field). In this paper, we propose to explore this phenomenon through construction of a topic influence graph (TIG) based on topic similarity between articles. More importantly, out of the set of possible TIGs, we determine an optimal TIG by using knowledge from citation graphs. Construction of TIG leverages traditional network analysis tools like community (sub-field) identification. In this paper, we construct the TIG on the ACL Anthology Network (AAN) dataset and leverage it to analyze the properties of seminal papers. Interestingly, we observe that seminal papers disseminate knowledge across different communities, trigger more research within its own community and apart from introducing new ideas, string together ideas from different communities.read more
Citations
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Journal ArticleDOI
Graggle: A Graph-based Approach to Document Clustering
I. J. King,H. Howie Huang +1 more
TL;DR: Graggle as mentioned in this paper uses graph autoencoder to generate low-dimensional vector embeddings from unlabeled documents and then leverages graph learning techniques to turn this graph into a highly efficient tool for dimensionality reduction.
Proceedings ArticleDOI
Graggle: A Graph-based Approach to Document Clustering
TL;DR: Graggle as discussed by the authors uses graph autoencoder to generate low-dimensional vector embeddings from unlabeled documents and then leverages graph learning techniques to turn this graph into a highly efficient tool for dimensionality reduction.
References
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Journal ArticleDOI
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Book ChapterDOI
A Discriminative Approach to Topic-Based Citation Recommendation
Jie Tang,Jing Zhang +1 more
TL;DR: A two-layer Restricted Boltzmann Machine model, called RBM-CS, is proposed, which can discover topic distributions of paper content and citation relationship simultaneously and can significantly outperform baseline methods for citation recommendation.
Proceedings ArticleDOI
Topic evolution and social interactions: how authors effect research
TL;DR: Applied to the CiteSeer dataset, a collection of documents in academia, the trends of research topics, how research topics are related and which are stable are shown and new ways for evaluating author impact are proposed.