Abstract
We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinct diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across communities, we show that it is possible to detect events, even when they do not trigger a significantly larger communication volume. We demonstrate the effectiveness of our method using two examples—the email communication network of Enron and the Twitter communication network during the Boston Marathon bombing.
Highlights
We propose a method for detecting large events based on the structure of temporal communication networks
Event detection is of crucial importance in many socio-technical systems because events often bear anomalous outcomes of societal interest[1], which range from unauthorized activities in computer networks[2], fraudulent credit card transactions[3] and disease outbreaks[4]
Instead of monitoring changes in the community structure itself, we propose to examine the difference between the ratio of inter- and the intra-community communication, supported by a previous finding that link information diffusion patterns with respect to communities to virality of the information[20]
Summary
We propose a method for detecting large events based on the structure of temporal communication networks. Communication traffic tends to vary based on particular dates (e.g., due to upcoming releases) Such variations represent a regular pattern of the email communication network and should not be associated to events[6]. Instead of monitoring changes in the community structure itself, we propose to examine the difference between the ratio of inter- and the intra-community communication, supported by a previous finding that link information diffusion patterns with respect to communities to virality of the information[20]. We demonstrate the effectiveness of the method by analyzing the email communication network of Enron (based on events reported in previous studies21,22) and the interactions between Twitter users during the Boston Marathon bombing
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