DeepNC: Deep Generative Network Completion

Abstract

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient DeepNC algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.

16 pages, 10 figures, 5 tables; to appear in the IEEE Transactions on Pattern Analysis and Machine Intelligence (Please cite our journal version that will appear in an upcoming issue.)

References in corpus (8)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Variational Graph Auto-Encoders
• Kronecker Graphs: An Approach to Modeling Networks
• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
• Learning Deep Generative Models of Graphs
• Efficient Graph Generation with Graph Recurrent Attention Networks
• Community Detection in Partially Observable Social Networks

Cited by in corpus (2)

• SCGG: A Deep Structure-Conditioned Graph Generative Model
• Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network