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A network embedding consists of a vector representation for each node in thenetwork. Its usefulness has been shown in many real-world application domains,such as social networks and web networks. Directed networks with textassociated with each node, such as software package dependency networks, arecommonplace. However, to the best of our knowledge, their embeddings havehitherto not been specifically studied. In this paper, we propose PCTADW-1 andPCTADW-2, two algorithms based on neural networks that learn embeddings ofdirected networks with text associated with each node. We create two newnode-labeled such networks: The package dependency networks in two popularGNU/Linux distributions, Debian and Fedora. We experimentally demonstrate thatthe embeddings produced by our algorithms resulted in node classification withbetter quality than those of various baselines on these two networks. Weobserve that there exist systematic presence of analogies (similar to those inword embeddings) in the network embeddings of software package dependencynetworks. To the best of our knowledge, this is the first time that suchsystematic presence of analogies is observed in network and documentembeddings. We further demonstrate that these network embeddings can be novellyused for better understanding software attributes, such as the developmentprocess and user interface of software, etc.

Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks