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Title: Fast Node Embeddings: Learning Ego-Centric Representations

Paper content:
Representation learning is one of the foundations of Deep Learning and allowed important improvements on several Machine Learning tasks, such as Neural Machine Translation, Question Answering and Speech Recognition.
Recent works have proposed new methods for learning representations for nodes and edges in graphs.
Several of these methods are based on the SkipGram algorithm, and they usually process a large number of multi-hop neighbors in order to produce the context from which node representations are learned.
In this paper, we propose an effective and also efficient method for generating node embeddings in graphs that employs a restricted number of permutations over the immediate neighborhood of a node as context to generate its representation, thus ego-centric representations.
We present a thorough evaluation showing that our method outperforms state-of-the-art methods in six different datasets related to the problems of link prediction and node classification, being one to three orders of magnitude faster than baselines when generating node embeddings for very large graphs.
Many important problems involving graphs require the use of learning algorithms to make predictions about nodes and edges.
These predictions and inferences on nodes and edges from a graph are typically done using classifiers with carefully engineered features BID13 .
These features, besides taking time and manual labor to be developed and acquired, usually do not generalize well to other problems or contexts.The field of Natural Language Processing (NLP) has had many advances due to the use of algorithms that learn word representations, instead of manually extracted features.
Originally proposed by BID5 and commonly used with Word2Vec algorithms like CBOW and SkipGram (Mikolov et al., 2013a) , word embeddings are used in many state-of-the-art solutions for neural machine translation (Luong & Manning, 2016; BID12 , question answering BID2 and natural language generation (Wen et al., 2015) .
Recent works have proposed new methods for learning representations for nodes and edges in graphs, based on random walks (Perozzi et al., 2014; BID13 or auto-encoding adjacency vectors (Wang et al., 2016) .In
this paper, we propose a new general purpose method for generating node embeddings in very large graphs, which we call Neighborhood Based Node Embeddings (or simply NBNE). NBNE
is based on the SkipGram algorithm and uses nodes neighborhoods as contexts. NBNE
outperforms state-of-the-art DeepWalk (Perozzi et al., 2014) and Node2Vec BID13 for the tasks of link prediction and node classification on six collections, being one to three orders of magnitude faster. In this
work, we considered DeepWalk and Node2Vec as baselines.The main reason for this improvement on effectiveness and efficiency is that we concentrate learning on the "predictable" parts of the graph. A study
by Facebook research BID8 found that each person in the world (at least among the people active on Facebook) is connected to every other person by an average 3.57 other people. In a graph
of this magnitude and connectedness, learning node embeddings by maximizing the log-probability of predicting nearby nodes in a random walk (with a window size of 5) can be highly inefficient and make it 'harder' for the embeddings to be constructed, even if these random walks are biased like in Node2Vec. We suspect
this can also make them more unstable, which would explain why they need more iterations before embedding convergence.
The proposed node embedding method NBNE shows results similar or better than the state-of-theart algorithms Node2Vec and DeepWalk on several different datasets.
It shows promising results in two application scenarios: link prediction and node classification, while being efficient and easy to compute for large graphs, differently from other node embedding algorithms, such as LINE (Tang et al., 2015) or SDNE (Wang et al., 2016) .
NBNE focuses learning on node's immediate neighbors, creating more ego-centric representations, which we suspect makes them more stable and faster to learn.
Empirical results show that, although it has a similar time complexity, NBNE can be trained in a fraction of the time taken by DeepWalk (10 to 190 times faster) or Node2Vec (200 to 6,000 times faster), giving fairly robust results.
Since embeddings are learned using only a node's immediate neighbors, we suspect it should also be easier to implement more stable asynchronous distributed algorithms to train them, and we leave this as future work.

assistant: text: A faster method for generating node embeddings that employs a number of permutations over a node's immediate neighborhood as context to generate its representation.