2 research outputs found
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
User behavior modeling is important for industrial applications such as
demographic attribute prediction, content recommendation, and target
advertising. Existing methods represent behavior log as a sequence of adopted
items and find sequential patterns; however, concrete location and time
information in the behavior log, reflecting dynamic and periodic patterns,
joint with the spatial dimension, can be useful for modeling users and
predicting their characteristics. In this work, we propose a novel model based
on graph neural networks for learning user representations from spatiotemporal
behavior data. A behavior log comprises a sequence of sessions; and a session
has a location, start time, end time, and a sequence of adopted items. Our
model's architecture incorporates two networked structures. One is a tripartite
network of items, sessions, and locations. The other is a hierarchical calendar
network of hour, week, and weekday nodes. It first aggregates embeddings of
location and items into session embeddings via the tripartite network, and then
generates user embeddings from the session embeddings via the calendar
structure. The user embeddings preserve spatial patterns and temporal patterns
of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It
adopts the attention mechanism to model complex interactions among the multiple
patterns in user behaviors. Experiments on real datasets (i.e., clicks on news
articles in a mobile app) show our approach outperforms strong baselines for
predicting missing demographic attributes
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Most graph neural network models learn embeddings of nodes in static
attributed graphs for predictive analysis. Recent attempts have been made to
learn temporal proximity of the nodes. We find that real dynamic attributed
graphs exhibit complex co-evolution of node attributes and graph structure.
Learning node embeddings for forecasting change of node attributes and birth
and death of links over time remains an open problem. In this work, we present
a novel framework called CoEvoGNN for modeling dynamic attributed graph
sequence. It preserves the impact of earlier graphs on the current graph by
embedding generation through the sequence. It has a temporal self-attention
mechanism to model long-range dependencies in the evolution. Moreover, CoEvoGNN
optimizes model parameters jointly on two dynamic tasks, attribute inference
and link prediction over time. So the model can capture the co-evolutionary
patterns of attribute change and link formation. This framework can adapt to
any graph neural algorithms so we implemented and investigated three methods
based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the
framework (and its methods) outperform strong baselines on predicting an entire
unseen graph snapshot of personal attributes and interpersonal links in dynamic
social graphs and financial graphs