Name
Abstract | ||
|---|---|---|
People's email communications can be modeled as graphs with vertices representing email accounts and edges representing email communications. Email communication data usually comes in as continuous data stream. Event detection aims to identify abnormal email communications that serve as analogs of real-world events imposed upon the data stream. The goal is to understand the communications behaviors of the subjects. The contents of emails are often not available or protected by privacy, which makes linkage information the only resource we can rely on. We propose a link-based event detection method that clusters vertices with similar communication patterns together and then, considers deviations from each vertex's individual profile, as well as its cluster profile. Experiments show that this method performs well on both Enron and our own email datasets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1529282.1529618 | SAC |
Keywords | Field | DocType |
continuous data stream,own email datasets,email account,event detection,email communication data,email communication,cluster profile,communications behavior,link-based event detection,abnormal email communication,email communication network,data stream,communication networks | Graph,World Wide Web,Telecommunications network,Vertex (geometry),Computer science,Data stream | Conference |
Citations | PageRank | References |
6 | 0.45 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaomeng Wan | 1 | 8 | 0.86 |
| Evangelos Milios | 2 | 3073 | 360.46 |
| Nauzer Kalyaniwalla | 3 | 31 | 2.68 |
| Jeannette Janssen | 4 | 295 | 32.23 |