General Graph Representation Learning Framework (DeepGL)
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that generalize across-networks and therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable features, and is space-efficient (by learning sparse feature vectors). In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of $\mathcal{O}(|E|)$, and scalable for large networks via an efficient parallel implementation. Compared with the state-of-the-art method, DeepGL is (1) effective for across-network transfer learning tasks and attributed graph representation learning, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 182x speedup in runtime performance, and (4) accurate with an average improvement of 20% or more on many learning tasks. …

Discover, Access, Distill (DAD)
DAD is comprised of:
• Discover: Find, identify the sources of good data, and the metrics. Sometimes request the data to be created (work with data engineers and business analysts)
• Access: Access the data. Sometimes via an API, a web crawler, an Internet download, a database access or sometimes in-memory within a database.
• Distill: Extract essence from data, the stuff that leads to decisions, increased ROI, and actions (such as determining optimum bid prices in an automated bidding system). It involves
• Exploring the data (creating a data dictionary and exploratory analysis)
• Cleaning (removing impurities)
• Refining (data summarization, sometimes multiple layers of summarization or hierarchical summarization)- Analyzing: statistical analyses (sometimes including stuff like experimental design that can take place even before the Access stage), both automated and manual. Might or might not require statistical modeling
• Presenting results or integrating results in some automated process …

Rosette
Rosette is an API for multilingual text analysis and information extraction. …