1 research outputs found
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks
Online social networks, World Wide Web, media and technological networks, and
other types of so-called information networks are ubiquitous nowadays. These
information networks are inherently heterogeneous and dynamic. They are
heterogeneous as they consist of multi-typed objects and relations, and they
are dynamic as they are constantly evolving over time. One of the challenging
issues in such heterogeneous and dynamic environments is to forecast those
relationships in the network that will appear in the future. In this paper, we
try to solve the problem of continuous-time relationship prediction in dynamic
and heterogeneous information networks. This implies predicting the time it
takes for a relationship to appear in the future, given its features that have
been extracted by considering both heterogeneity and temporal dynamics of the
underlying network. To this end, we first introduce a feature extraction
framework that combines the power of meta-path-based modeling and recurrent
neural networks to effectively extract features suitable for relationship
prediction regarding heterogeneity and dynamicity of the networks. Next, we
propose a supervised non-parametric approach, called Non-Parametric Generalized
Linear Model (NP-GLM), which infers the hidden underlying probability
distribution of the relationship building time given its features. We then
present a learning algorithm to train NP-GLM and an inference method to answer
time-related queries. Extensive experiments conducted on synthetic data and
three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate
the effectiveness of NP-GLM in solving continuous-time relationship prediction
problem vis-a-vis competitive baselinesComment: To appear in ACM Transactions on Knowledge Discovery from Dat