Temporal Network Representation Learning via Historical Neighborhoods Aggregation

• URL: http://arxiv.org/abs/2003.13212v1
• Date: Mon, 30 Mar 2020 04:18:48 GMT
• Title: Temporal Network Representation Learning via Historical Neighborhoods Aggregation
• Authors: Shixun Huang, Zhifeng Bao, Guoliang Li, Yanghao Zhou, J.Shane Culpepper
• Abstract summary: We propose the Embedding via Historical Neighborhoods Aggregation (EHNA) algorithm. We first propose a temporal random walk that can identify relevant nodes in historical neighborhoods. Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings.
• Score: 28.397309507168128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node classification, and link prediction. Although significant progress has been made on this problem in recent years, several important challenges remain, such as how to properly capture temporal information in evolving networks. In practice, most networks are continually evolving. Some networks only add new edges or nodes such as authorship networks, while others support removal of nodes or edges such as internet data routing. If patterns exist in the changes of the network structure, we can better understand the relationships between nodes and the evolution of the network, which can be further leveraged to learn node representations with more meaningful information. In this paper, we propose the Embedding via Historical Neighborhoods Aggregation (EHNA) algorithm. More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings that directly capture temporal information in the underlying feature representation. We perform extensive experiments on a range of real-world datasets, and the results demonstrate the effectiveness of our new approach in the network reconstruction task and the link prediction task.

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