27,321 research outputs found
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
Online social networks have gained great success in recent years and many of
them involve multiple kinds of nodes and complex relationships. Among these
relationships, social links among users are of great importance. Many existing
link prediction methods focus on predicting social links that will appear in
the future among all users based upon a snapshot of the social network. In
real-world social networks, many new users are joining in the service every
day. Predicting links for new users are more important. Different from
conventional link prediction problems, link prediction for new users are more
challenging due to the following reasons: (1) differences in information
distributions between new users and the existing active users (i.e., old
users); (2) lack of information from the new users in the network. We propose a
link prediction method called SCAN-PS (Supervised Cross Aligned Networks link
prediction with Personalized Sampling), to solve the link prediction problem
for new users with information transferred from both the existing active users
in the target network and other source networks through aligned accounts. We
proposed a within-target-network personalized sampling method to process the
existing active users' information in order to accommodate the differences in
information distributions before the intra-network knowledge transfer. SCAN-PS
can also exploit information in other source networks, where the user accounts
are aligned with the target network. In this way, SCAN-PS could solve the cold
start problem when information of these new users is total absent in the target
network.Comment: 11 pages, 10 figures, 4 table
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
Weighted Random Walk Sampling for Multi-Relational Recommendation
In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many
real-world applications. However, existing methods mainly focus on networks
with single-typed nodes/edges and cannot scale well to handle large networks.
Many real-world networks consist of billions of nodes and edges of multiple
types, and each node is associated with different attributes. In this paper, we
formalize the problem of embedding learning for the Attributed Multiplex
Heterogeneous Network and propose a unified framework to address this problem.
The framework supports both transductive and inductive learning. We also give
the theoretical analysis of the proposed framework, showing its connection with
previous works and proving its better expressiveness. We conduct systematical
evaluations for the proposed framework on four different genres of challenging
datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results
demonstrate that with the learned embeddings from the proposed framework, we
can achieve statistically significant improvements (e.g., 5.99-28.23% lift by
F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link
prediction. The framework has also been successfully deployed on the
recommendation system of a worldwide leading e-commerce company, Alibaba Group.
Results of the offline A/B tests on product recommendation further confirm the
effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn
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