document: Community detection in incomplete information networks With the recent advances in information networks , the problem of community detection has attracted much attention in the last decade . While network community detection has been ubiquitous , the task of collecting complete network data remains challenging in many real-world applications . Usually the collected network is incomplete with most of the edges missing . Commonly , in such networks , all nodes with attributes are available while only the edges within a few local regions of the network can be observed . In this paper , we study the problem of detecting communities in incomplete information networks with missing edges . We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions . We then use the learned distance metric to estimate the distance between any pair of nodes in the network . A hierarchical clustering approach is proposed to detect communities within the incomplete information networks . Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks .
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