Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
- URL: http://arxiv.org/abs/2007.13004v1
- Date: Sat, 25 Jul 2020 20:07:28 GMT
- Title: Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
- Authors: Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh
V. Chawla, Meng Jiang
- Abstract summary: We present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
It preserves the impact of earlier graphs on the current graph by embedding generation through the sequence.
It has a temporal self-attention mechanism to model long-range dependencies in the evolution.
- Score: 28.848851822725933
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most graph neural network models learn embeddings of nodes in static
attributed graphs for predictive analysis. Recent attempts have been made to
learn temporal proximity of the nodes. We find that real dynamic attributed
graphs exhibit complex co-evolution of node attributes and graph structure.
Learning node embeddings for forecasting change of node attributes and birth
and death of links over time remains an open problem. In this work, we present
a novel framework called CoEvoGNN for modeling dynamic attributed graph
sequence. It preserves the impact of earlier graphs on the current graph by
embedding generation through the sequence. It has a temporal self-attention
mechanism to model long-range dependencies in the evolution. Moreover, CoEvoGNN
optimizes model parameters jointly on two dynamic tasks, attribute inference
and link prediction over time. So the model can capture the co-evolutionary
patterns of attribute change and link formation. This framework can adapt to
any graph neural algorithms so we implemented and investigated three methods
based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the
framework (and its methods) outperform strong baselines on predicting an entire
unseen graph snapshot of personal attributes and interpersonal links in dynamic
social graphs and financial graphs.
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