  Link prediction is a key problem for network-structured data, attracting
considerable research efforts owing to its diverse applications. The current
link prediction methods focus on general networks and are overly dependent on
either the closed triangular structure of networks or node attributes. Their
performance on sparse or highly hierarchical networks has not been well
studied. On the other hand, the available tree-like benchmark datasets are
either simulated, with limited node information, or small in scale. To bridge
this gap, we present a new benchmark dataset TeleGraph, a highly sparse and
hierarchical telecommunication network associated with rich node attributes,
for assessing and fostering the link inference techniques. Our empirical
results suggest that most of the algorithms fail to produce a satisfactory
performance on a nearly tree-like dataset, which calls for special attention
when designing or deploying the link prediction algorithm in practice.
