Uncovering, Understanding, and Predicting Links

Abstract

Network data, such as citation networks of documents, hyperlinked

networks of web pages, and social networks of friends, are pervasive

in applied statistics and machine learning. The statistical analysis

of network data can provide both useful predictive models and

descriptive statistics. Predictive models can point social network

members towards new friends, scientific papers towards relevant

citations, and web pages towards other related pages. Descriptive

statistics can uncover the hidden community structure underlying a

network data set.

In this work we develop new models of network data that account for

both links and attributes. We also develop the inferential and predictive

tools around these models to make them widely applicable to large, real-world

data sets. One such model, the Relational Topic Model can predict links

using only a new node's attributes. Thus, we can suggest citations of newly

written papers, predict the likely hyperlinks of a web page in development, or

suggest friendships in a social network based only on a new user's profile of

interests. Moreover, given a new node and its links, the model

provides a predictive distribution of node attributes. This mechanism

can be used to predict keywords from citations or a user's interests

from his or her social connections.

While explicit network data --- network data in which the connections between

people, places, genes, corporations, etc. are explicitly encoded --- are

already ubiquitous, most of these can only annotate

connections in a limited fashion. Although relationships between entities are

rich, it is impractical to manually devise complete characterizations of these

relationships for every pair of entities on large, real-world corpora.

To resolve this we present a probabilistic topic model to analyze

text corpora and infer descriptions of its entities and of relationships

between those entities. We show qualitatively and quantitatively that our

model can construct and annotate graphs of relationships and make useful

predictions.