Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks

In practice, however, downstream tasks such as has gained a lot of attention in recent years [1, 5, 10, 31, 35, 37], link prediction require specific contextual information that can where a low-dimensional vector representation of each node in be extracted from the subgraphs related to the nodes provided as the graph is used for downstream applications such as link prediction input to the task. To tackle this challenge, we develop SLiCE, a [1, 5, 39] or multi-hop reasoning [8, 13, 42]. Many of the framework bridging static representation learning methods using existing methods focus on obtaining a static vector representation global information from the entire graph with localized attention per node that is agnostic to any specific context and is typically driven mechanisms to learn contextual node representations. We obtained by learning the importance of all of the node's immediate first pre-train our model in a self-supervised manner by introducing and multi-hop neighbors in the graph. However, we argue higher-order semantic associations and masking nodes, and that nodes in a heterogeneous network exhibit a different behavior, then fine-tune our model for a specific link prediction task. Instead based on different relation types and their participation in diverse of training node representations by aggregating information from network communities. Further, most downstream tasks such as link all semantic neighbors connected via metapaths, we automatically prediction are dependent on the specific contextual information learn the composition of different metapaths that characterize the related to the input nodes that can be extracted in the form of task context for a specific task without the need for any predefined specific subgraphs.