Proposal 2nd Draft Nitin Yandong Ming Yanbo

Modeling Academic Collaboration and Influence in scholarly literature

Team members

Nitin Agarwal

Yandong Liu

Yanbo Xu

Ming Sun

The Problem

New research papers are growing rapidly, especially in computer science field, making it hard to follow. Instead of wasting time reading all the papers, we want our computers to answer following questions:

• Who to collaborate with?
• Which work to cite?
• Who to review this paper (for conference organizers)?

Essentially, we d like to capture the interactions and relationships between people. For academia, it s mainly about collaboration and citation. There are approaches about content Analysis and/or connectivity Analysis.

Related work

Author Topic Model

Author-Topic model describes such a generative process about how each document is generated:

For each document:

• Choose an author
• Choose a topic
• Choose a word

The result obtained includes the topic distribution per each author, and word distribution per each topic. One possible application suggested by this paper is to find related authors by computing KL-divergence of different author's topic distribution.

Author-Recipient-Topic Model

For this model authors believe that nodes have different roles like in email data there are senders and receivers and they should be treated differently in the model. Therefore instead of modeling individuals, we model the pair relationship directly. An author and a set of recipients are observed. Topics are now conditioned on (author, recipient) pair.

As we can see a lot of previous work was either based on content analysis, or graph connectivity analysis. There is tremendously rich information hidden in the text so we'll go with topic model. We will derive a hybrid model that utilizes knowledge of both kinds. Similarly, we model the pair relationship directly, such as (author, author) or (author, citation)

Collaboration Influence Model

This proposed model is influenced by earlier work in topic models which tend to uncover the social structure in text and discover latent topics conditioned on it. Some of the important characteristics of this topic model are:

• A biased Bernoulli flip which favors collaboration over influence
• Dirichlet priors over topics
• Each word sampled has an author-pair label and a relation label
• The relation label specifies whether the word resulted due to collaboration or influence

The aspects of a corpus of networked scientific article that the collaboration influence model tries to capture are author network and collaboration vs influence relations.

Dataset

We would be working with the ACL Anthology 2008 (Radev et al.) dataset. Some important statistics of the dataset are :

• Contains 13, 739 papers from computational linguistics conference
• 10,409 nodes in author citation network
• 195,504 edges in author citation network
• 10,409 nodes in author collaboration network
• 57,614 edges in author collaboration network

Application

Who to collaborate with?

• Given a professor's name and his/her research topic, we want the computer to list the most possible researchers for him/her to collaborate.
• This can be stated as $a_{r}=argmax_{a_{r}^{*}}P(a_{r}^{*},r=co-author|a,z)$
• Here, $a$ means the author of a paper. $a_{r}$ could be either a co-author or citee. This role depends on the parameter $r$. We use 'co-author' and 'influence' to represent the co-authorship and citation. The research topic is denoted as $z$.

Who to cite?

• Given a research topic, we want the computer to recommend the most influential author(s) in this area.
• This can be stated as $a_{r}=argmax_{a_{r}^{*}}P(a_{r}^{*},r=influence|z)$.