EDGE2VEC: Edge Representations for Large-Scale Scalable Hierarchical Learning

Abstract

In present front-line of Big Data, prediction tasks over the nodes and edges in complex deep architecture needs a careful representation of features by assigning hundreds of thousands, or even millions of labels and samples for information access system, especially for hierarchical extreme multi-label classification. We introduce edge2vec, an edge representations framework for learning discrete and continuous features of edges in deep architecture. In edge2vec, we learn a mapping of edges associated with nodes where random samples are augmented by statistical and semantic representations of words and documents. We argue that infusing semantic representations of features for edges by exploiting word2vec and para2vec is the key to learningricherrepresentationsforexploringtargetnodes or labels in the hierarchy. Moreover, we design and implement a balanced stochastic dual coordinate ascent (DCA)-based support vector machine for speeding up training. We introduce a global decision-based top-down walks instead of random walks to predict the most likelihood labels in the deep architecture. We judge the efficiency of edge2vec over the existing state-of-the-art techniques on extreme multi-label hierarchical as well as flat classification tasks. The empirical results show that edge2vec is very promising and computationally very efficient in fast learning and predicting tasks. In deep learning workbench, edge2vec represents a new direction for statistical and semantic representations of features in task-independent networks.