Adapting PageRank to Position Events in Time
In this paper, we order events in time by using evidence present in their partial orders. We propose an algorithm named TimeRank, a variant of PageRank, for this task. PageRank operates on the hyperlink graph and orders the web pages according to their importance. We identify limitations of PageRank in the context of temporally ordering the nodes. We draw an analogy between the notion of importance in PageRank to the notion of recency in TimeRank. We evaluate TimeRank using the Citation Graph of scientific publications of physics and propose a baseline method to compare TimeRank and PageRank. The baseline method ranks the nodes according to their number of immediate predecessors without considering the higher order transitive relations among the events. Evaluation results suggest that TimeRank outperforms both the baseline method and PageRank in this task.
- 2.Jiang, H., Ge, Y.-X., Zuo, D., Han, B.: Timerank: a method of improving ranking scores by visited time. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1654–1657 (2008)Google Scholar
- 4.Mani, I., Schiffman, B., Zhang, J.: Inferring temporal ordering of events in news. In: Proceedings of North American chapter of the ACL on human language technology (HLT-NAACL 2003), pp. 55–57. ACL (2003)Google Scholar
- 5.O’Madadhain, J., Smyth, P.: Eventrank: a framework for ranking time-varying networks. In: Proceedings of the 3rd international workshop on Link discovery, pp. 9–16. ACM (2005)Google Scholar
- 6.Page, L., Brin, S., Motwani, R., Winograd, T.: The Pagerank citation ranking: bringing order to the web (1999)Google Scholar
- 7.Vrotsou, K.: Everyday mining: exploring sequences in event-based data (2010)Google Scholar