Learning From Graph Neighborhoods Using LSTMs
This addresses the need for automated prediction in graph-based problems across domains like finance and online platforms, though it is incremental as it adapts existing LSTM techniques to graph neighborhoods.
The paper tackles the problem of making predictions from local graph neighborhoods by introducing an LSTM-based method that learns directly from data, bypassing manual feature engineering, and demonstrates its effectiveness on synthetic and real-world datasets including crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.
Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.