10,446 research outputs found
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
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
- …