Bibliographic Analysis with the Citation Network Topic Model

09/22/2016
by   Kar Wai Lim, et al.
0

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/21/2016

Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network

Bibliographic analysis considers the author's research areas, the citati...
research
12/12/2022

Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks

Information spread on networks can be efficiently modeled by considering...
research
11/27/2012

A simple non-parametric Topic Mixture for Authors and Documents

This article reviews the Author-Topic Model and presents a new non-param...
research
11/30/2020

A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces

Authorial clustering involves the grouping of documents written by the s...
research
04/24/2022

Co-citation and Co-authorship Networks of Statisticians

We collected and cleaned a large data set on publications in statistics....
research
05/20/2023

Paragraph-level Citation Recommendation based on Topic Sentences as Queries

Citation recommendation (CR) models may help authors find relevant artic...
research
06/07/2021

Scientific Dataset Discovery via Topic-level Recommendation

Data intensive research requires the support of appropriate datasets. Ho...

Please sign up or login with your details

Forgot password? Click here to reset