Link direction for link prediction

Ke ke Shang, Michael Small, Wei sheng Yan

    Research output: Contribution to journalArticlepeer-review

    40 Citations (Scopus)

    Abstract

    Almost all previous studies on link prediction have focused on using the properties of the network to predict the existence of links between pairs of nodes. Unfortunately, previous methods rarely consider the role of link direction for link prediction. In fact, many real-world complex networks are directed and ignoring the link direction will mean overlooking important information. In this study, we propose a phase-dynamic algorithm of the directed network nodes to analyse the role of link directions and demonstrate that the bi-directional links and the one-directional links have different roles in link prediction and network structure formation. From this, we propose new directional prediction methods and use six real networks to test our algorithms. In real networks, we find that compared to a pair of nodes which are connected by a one-directional link, a pair of nodes which are connected by a bi-directional link always have higher probabilities to connect to the common neighbours with only bi-directional links (or conversely by one-directional links). We suggest that, in the real networks, the bi-directional links will generally be more informative for link prediction and network structure formation. In addition, we propose a new directional randomized algorithm to demonstrate that the direction of the links plays a significant role in link prediction and network structure formation.

    Original languageEnglish
    Pages (from-to)767-776
    Number of pages10
    JournalPhysica A: Statistical Mechanics and its Applications
    Volume469
    DOIs
    Publication statusPublished - 1 Mar 2017

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