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Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali · Arman Hasanzadeh · Krishna Narayanan · Nick Duffield · Mingyuan Zhou · Xiaoning Qian

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #147

Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph recurrent neural network (GRNN) to capture both topology and node attribute changes in dynamic graphs. We argue that the use of high-level latent random variables in this variational GRNN (VGRNN) can better capture potential variability observed in dynamic graphs as well as the uncertainty of node latent representation. With semi-implicit variational inference developed for this new VGRNN architecture (SI-VGRNN), we show that flexible non-Gaussian latent representations can further help dynamic graph analytic tasks. Our experiments with multiple real-world dynamic graph datasets demonstrate that SI-VGRNN and VGRNN consistently outperform the existing baseline and state-of-the-art methods by a significant margin in dynamic link prediction.

Author Information

Ehsan Hajiramezanali (Texas A&M University)
Arman Hasanzadeh (Texas A&M University)
Krishna Narayanan (Texas A&M University)
Nick Duffield (Texas A&M University)
Mingyuan Zhou (University of Texas at Austin)
Xiaoning Qian (Texas A&M)

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