  Graph is a natural representation of data for a variety of real-word
applications, such as knowledge graph mining, social network analysis and
biological network comparison. For these applications, graph embedding is
crucial as it provides vector representations of the graph. One limitation of
existing graph embedding methods is that their embedding optimization
procedures are disconnected from the target application. In this paper, we
propose a novel approach, namely Customized Graph Embedding (CGE) to tackle
this problem. The CGE algorithm learns customized vector representations of
graph nodes by differentiating the importance of distinct graph paths
automatically for a specific application. Extensive experiments were carried
out on a diverse set of node classification datasets, which demonstrate strong
performances of CGE and provide deep insights into the model.
