  With the development of temporal networks such as E-commerce networks and
social networks, the issue of temporal link prediction has attracted increasing
attention in recent years. The Temporal Link Prediction task of WSDM Cup 2022
expects a single model that can work well on two kinds of temporal graphs
simultaneously, which have quite different characteristics and data properties,
to predict whether a link of a given type will occur between two given nodes
within a given time span. Our team, named as nothing here, regards this task as
a link prediction task in heterogeneous temporal networks and proposes a
generic model, i.e., Heterogeneous Temporal Graph Network (HTGN), to solve such
temporal link prediction task with the unfixed time intervals and the diverse
link types. That is, HTGN can adapt to the heterogeneity of links and the
prediction with unfixed time intervals within an arbitrary given time period.
To train the model, we design a Bi-Time-Window training strategy (BTW) which
has two kinds of mini-batches from two kinds of time windows. As a result, for
the final test, we achieved an AUC of 0.662482 on dataset A, an AUC of 0.906923
on dataset B, and won 2nd place with an Average T-scores of 0.628942.

Social and Information Networks;Artificial Intelligence;Machine Learning