TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
This addresses the gap in evaluating link prediction methods for hierarchical networks, which is incremental as it provides a new dataset rather than a novel method.
The authors tackled the problem of link prediction on sparse, hierarchical networks by introducing TeleGraph, a benchmark dataset from a telecommunication network with rich node attributes, and found that most existing algorithms perform poorly on such tree-like structures.
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.