30 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
Fake News Detection with Deep Diffusive Network Model
In recent years, due to the booming development of online social networks,
fake news for various commercial and political purposes has been appearing in
large numbers and widespread in the online world. With deceptive words, online
social network users can get infected by these online fake news easily, which
has brought about tremendous effects on the offline society already. An
important goal in improving the trustworthiness of information in online social
networks is to identify the fake news timely. This paper aims at investigating
the principles, methodologies and algorithms for detecting fake news articles,
creators and subjects from online social networks and evaluating the
corresponding performance. This paper addresses the challenges introduced by
the unknown characteristics of fake news and diverse connections among news
articles, creators and subjects. Based on a detailed data analysis, this paper
introduces a novel automatic fake news credibility inference model, namely
FakeDetector. Based on a set of explicit and latent features extracted from the
textual information, FakeDetector builds a deep diffusive network model to
learn the representations of news articles, creators and subjects
simultaneously. Extensive experiments have been done on a real-world fake news
dataset to compare FakeDetector with several state-of-the-art models, and the
experimental results have demonstrated the effectiveness of the proposed model
Personalized Financial News Recommendation Algorithm Based on Ontology
AbstractTo deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender