Model for Heterogeneous Topic web (MHT)

This is a C++ implementation of MHT (Model for Heterogeneous Topic web).
More details about MHT are in the paper[pdf]:

Junxian He, Ying Huang, Changfeng Liu, Jiaming Shen, Yuting Jia, Xinbing Wang, "Text Network Exploration via Heterogeneous Web of Topics", accepted into ICDM 2016 Workshop on Data Mining in Networks

The demo TopicAtlas illustrated in the paper can be found here.

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Data Format

abstract_ID.txt

The first line contains the number of documents.
From the second line to the last, each line represents a document, i.e., line i+1 represents document vi. The format for each line is:
[Word ID] [Word Count] [Word ID] [Word Count] .....repeatedly

abstract.txt

line i represents the abstract of a document vi.

reference.txt

line i represents the reference list (document ID) of
document vi

title.txt

line i represents the title of document vi

wordmap.dat

It contains the mapping relationship from vocabulary to 
WordID. Each line expresses a word, the format is: 
[Word] [ID]

Compiling

Type "make" in a shell.

Model Learning

Estimate the model by executing:

main [dataset] [WordTopic num] [DocTopic num] [convergence]

dataset: dataset name 
WordTopic num: number of WordTopics  
DocTopic num: number of DocTopics  
convergence: the convergence threshold of inner variational iteration  
loop  

The code would produce an output folder in its parent directory and save models in four files in that directory:

1. final.other contains alpha.
2. final.beta contains the WordTopic distributions. (Each line is a WordTopic; in line k, each entry is p(w | z=k)
3. final.omega contains the DocTopic distributions. (Each line is a DocTopic; in line k', each entry is p(y | z'=k')
4. final.eta contains the transition DocTopic distributions. (Each line is a WordTopic; in line k, each entry is p(z' | z=k)

Reference

@inproceedings{he2016text,
  title={Text Network Exploration via Heterogeneous Web of Topics},
  author={He, Junxian and Huang, Ying and Liu, Changfeng and Shen, Jiaming and Jia, Yuting and Wang, Xinbing},
  booktitle={Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on},
  pages={99--106},
  year={2016},
  organization={IEEE}
}