8,041 research outputs found
Locality statistics for anomaly detection in time series of graphs
The ability to detect change-points in a dynamic network or a time series of
graphs is an increasingly important task in many applications of the emerging
discipline of graph signal processing. This paper formulates change-point
detection as a hypothesis testing problem in terms of a generative latent
position model, focusing on the special case of the Stochastic Block Model time
series. We analyze two classes of scan statistics, based on distinct underlying
locality statistics presented in the literature. Our main contribution is the
derivation of the limiting distributions and power characteristics of the
competing scan statistics. Performance is compared theoretically, on synthetic
data, and on the Enron email corpus. We demonstrate that both statistics are
admissible in one simple setting, while one of the statistics is inadmissible a
second setting.Comment: 15 pages, 6 figure
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
Network embedding aims at projecting the network data into a low-dimensional
feature space, where the nodes are represented as a unique feature vector and
network structure can be effectively preserved. In recent years, more and more
online application service sites can be represented as massive and complex
networks, which are extremely challenging for traditional machine learning
algorithms to deal with. Effective embedding of the complex network data into
low-dimension feature representation can both save data storage space and
enable traditional machine learning algorithms applicable to handle the network
data. Network embedding performance will degrade greatly if the networks are of
a sparse structure, like the emerging networks with few connections. In this
paper, we propose to learn the embedding representation for a target emerging
network based on the broad learning setting, where the emerging network is
aligned with other external mature networks at the same time. To solve the
problem, a new embedding framework, namely "Deep alIgned autoencoder based
eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link
and attribute in a unified analytic based on broad learning, and introduces the
multiple aligned attributed heterogeneous social network concept to model the
network structure. A set of meta paths are introduced in the paper, which
define various kinds of connections among users via the heterogeneous link and
attribute information. The closeness among users in the networks are defined as
the meta proximity scores, which will be fed into DIME to learn the embedding
vectors of users in the emerging network. Extensive experiments have been done
on real-world aligned social networks, which have demonstrated the
effectiveness of DIME in learning the emerging network embedding vectors.Comment: 10 pages, 9 figures, 4 tables. Full paper is accepted by ICDM 2017,
In: Proceedings of the 2017 IEEE International Conference on Data Mining
- …