A Holistic Approach for Predicting Links in Coevolving Multilayer
Networks
release_iamtmjh55netzjom6c75avdcki
by
Alireza Hajibagheri, Gita Sukthankar, Kiran Lakkaraju
2016
Abstract
Networks extracted from social media platforms frequently include multiple
types of links that dynamically change over time; these links can be used to
represent dyadic interactions such as economic transactions, communications,
and shared activities. Organizing this data into a dynamic multiplex network,
where each layer is composed of a single edge type linking the same underlying
vertices, can reveal interesting cross-layer interaction patterns. In
coevolving networks, links in one layer result in an increased probability of
other types of links forming between the same node pair. Hence we believe that
a holistic approach in which all the layers are simultaneously considered can
outperform a factored approach in which link prediction is performed separately
in each layer. This paper introduces a comprehensive framework, MLP (Multilayer
Link Prediction), in which link existence likelihoods for the target layer are
learned from the other network layers. These likelihoods are used to reweight
the output of a single layer link prediction method that uses rank aggregation
to combine a set of topological metrics. Our experiments show that our
reweighting procedure outperforms other methods for fusing information across
network layers.
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