Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Event Detection on Dynamic Graphs   Event detection is a critical task for timely decision-making in graph
analytics applications. Despite the recent progress towards deep learning on
graphs, event detection on dynamic graphs presents particular challenges to
existing architectures. Real-life events are often associated with sudden
deviations of the normal behavior of the graph. However, existing approaches
for dynamic node embedding are unable to capture the graph-level dynamics
related to events.
  In this paper, we propose DyGED, a simple yet novel deep learning model for
event detection on dynamic graphs. DyGED learns correlations between the graph
macro dynamics -- i.e. a sequence of graph-level representations -- and labeled
events. Moreover, our approach combines structural and temporal self-attention
mechanisms to account for application-specific node and time importances
effectively. Our experimental evaluation, using a representative set of
datasets, demonstrates that DyGED outperforms competing solutions in terms of
event detection accuracy by up to 8.5% while being more scalable than the top
alternatives. We also present case studies illustrating key features of our
model.

14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con