Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Clustering in Dynamic Networks with Tensor Decomposition   Dynamic networks are increasingly being usedd to model real world datasets. A
challenging task in their analysis is to detect and characterize clusters. It
is useful for analyzing real-world data such as detecting evolving communities
in networks. We propose a temporal clustering framework based on a set of
network generative models to address this problem. We use PARAFAC decomposition
to learn network models from datasets.We then use $K$-means for clustering, the
Silhouette criterion to determine the number of clusters, and a similarity
score to order the clusters and retain the significant ones. In order to
address the time-dependent aspect of these clusters, we propose a segmentation
algorithm to detect their formations, dissolutions and lifetimes. Synthetic
networks with ground truth and real-world datasets are used to test our method
against state-of-the-art, and the results show that our method has better
performance in clustering and lifetime detection than previous methods.

29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph