Citation prediction using time series approach KDD Cup 2003 (task 1)

Abstract

In this article we describe our experiences in building the winning system for KDD Cup, 2003, Task 1. This year's competition was based on a very large archive of research papers that provides an unusually comprehensive snapshot of a particular social network in action; in addition to the full text of research papers, it includes both explicit citation structure and partial data on the downloading of papers by users. It provides a framework for testing general network and usage mining techniques, which can be explored via four varied and interesting tasks. Each task is a separate competition with its own specific goal. In task 1 the goal is to predict the change in number of citations to each paper in the archive over time.The contest was very challenging because the given data was not in a format suitable for conventional data mining techniques. So we had to do a considerable amount of data processing. Also there were different sources of data like tex files, citation graph, slac-data database. So we had to make a decision about which sources to use and how much to use.