23/08/2020

Laplacian change point detection for dynamic graphs

Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau, Reihaneh Rabbany

Keywords: change point detection, dynamic graphs, spectral methods, anomaly detection

Abstract Paper Similar Papers

Abstract: Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. LAD explicitly models short term and long term dependencies by applying two sliding windows. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network. In all three datasets, we demonstrate that our method can more effectively identify anomalous time points according to significant real world events.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3394486.3403077#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at KDD 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 3:09 
 0
 0
 0
 0
 13:27 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 16:52 
 0
 0
 0
 0
 7:43 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 19:51 
 0
 0
 0
 0
 13:36 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 7:45 
 0
 0
 0
 0
 18:52 
 0
 0
 0
 0
 16:46 
 0
 0
 0
 0
 8:59 
 0
 0
 0
 1
 15:49 
 0
 0
 0
 0
 6:21 
 0
 0
 0
 0
 4:22 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 2:57 
 0
 0
 0
 0
 15:25 
 0
 0
 0
 0
 3:23 
 0
 0
 0
 0
 15:19 
 0
 0
 0
 0
 3:04 
 0
 0
 0
 0
 4:40 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 14:55 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 2:53 
 0
 0
 0
 0
 12:20 
 0
 0
 0
 0
 9:20 
 0
 0
 0
 0
 11:06 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 17:45 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 20:08 
 0
 0
 0
 0
 13:46 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 15:07 
 0
 0
 0
 0
 5:28