论文标题
HTGN-BTW：与时间链接预测的双级窗口培训策略的异质时间图网络
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
论文作者
论文摘要
随着电子商务网络和社交网络等时间网络的发展，近年来，时间链接预测的问题引起了人们的关注。 WSDM CUP 2022的时间链接预测任务期望一个单个模型可以同时在两种时间表上很好地工作，这些模型具有完全不同的特性和数据属性，以预测给定类型的链接是否会在给定时间跨度内的两个给定节点之间发生。我们的团队在这里被命名为“ Noth”，将此任务视为异质时间网络中的链接预测任务，并提出了一个通用模型，即异质的时间图网络（HTGN），以使用未使用的时间间隔和多样的链接来解决此类的时间链接预测任务。也就是说，HTGN可以适应链接的异质性，并在任意给定时间段内使用未连接的时间间隔进行预测。为了训练该模型，我们设计了两种时间窗口的两种迷你批次。结果，对于最终测试，我们在数据集A上达到了0.662482的AUC，在数据集B上的AUC为0.906923，并以平均T分数为0.628942赢得了第二名。
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.
