Task-Guided Context-Path Embedding in Temporal Heterogeneous Networks

Network embedding maps the nodes of a network to a continuous vector space, which can then be used as the input to downstream tasks, such as node classification, node clustering, link prediction, and similarity search. To learn network embedding more effectively, many technologies adopt the approach of random walk to obtain the network structure. As the meta-path of heterogeneous networks emerges, network embedding will be equipped with more semantic interpretation. Consequently, various random walks, based on meta-path strategies, have been proposed for network embedding. However, the combination of semantic and structure in a heterogeneous network cannot achieve ideal results. To overcome this challenge, we start from a task-guided issue by combining the timestamps information in the heterogeneous network, and then employing the method of temporal segmentation to decompose the network into a continuous temporal sequence. Finally, the set of context-paths between nodes is calculated in a continuous vector by the depth-first meta-path search algorithm. More precisely, we propose a Temporal Sliding Density Walk (TSDW) algorithm by combining network semantics and structure effectively. Empirical results for network data show that TSDW could significantly outperform the state-of-the-art representation learning models, including DeepWalk, LINE, Node2vec, PTE, Meapath2vec, HIN2vec, HTNE, and CTDNE by 3.02% to 44.9% of Macro-F1, 0.9% to 18.92% of Micro-F1 in multi-class node classification and 21% to 47% of NMI in node clustering.