I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly Detection".
I would love to replicate it on a different data set, but I'm not familiar enough with the methods to know how to use them. Let's say I have a series of snapshots of network of nodes across a period of six months. The nodes have a long-tailed degree distribution, with most having only a few connections, but some having a great many. New nodes appear within this time period.
How could I implement sequentially discounted normalized maximum likelihood calculations used in the paper to detect anomalous links that I think might be precursors to a burst? Are there other methods that would be more appropriate?
I ask both theoretically and practically. If someone could point me to a way to implement this in python or R, that would be very helpful.
Anyone? I know you smart folks out there have some starting thoughts for an answer,