{'fraction_non_alphanumeric': 0.014341590612777053, 'fraction_numerical': 0.0, 'mean_word_length': 5.4, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Measuring Graph Reconstruction Precisions: How Well Do Embeddings Preserve the Graph Proximity Structure?

Graph embedding aims at learning representations of nodes in a low dimensional vector space. Good embeddings should preserve proximity structure of the original graph and thus are expected to accurately reconstruct the graph. We propose a reconstruction procedure such that the reconstructed graph keeps the total number of weights of the original one. Then we assess the reconstruction precision using a global view based graph similarity metric called DeltaCon. Based on this metric, we found that the embeddings by the state-of-the-art techniques can only preserve part of the proximity structure and is insufficient to achieve high reconstruction accuracy.