GraphDBLP: a system for analysing networks of computer scientists through graph databases



This paper presents GraphDBLP, a system that models the DBLP bibliography as a graph database for performing graph-based queries and social network analyses. GraphDBLP also enriches the DBLP data through semantic keyword similarities computed via word-embedding. In this paper, we discuss how the system was formalized as a multi-graph, and how similarity relations were identified through word2vec. We also provide three meaningful queries for exploring the DBLP community to (i) investigate author profiles by analysing their publication records; (ii) identify the most prolific authors on a given topic, and (iii) perform social network analyses over the whole community. To date, GraphDBLP contains 5+ million nodes and 24+ million relationships, enabling users to explore the DBLP data by referencing more than 3.3 million publications, 1.7 million authors, and more than 5 thousand publication venues. Through the use of word-embedding, more than 7.5 thousand keywords and related similarity values were collected. GraphDBLP was implemented on top of the Neo4j graph database. The whole dataset and the source code are publicly available to foster the improvement of GraphDBLP in the whole computer science community.

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    In this work, venues include conferences and journals.

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    A multi-graph is a graph where multiple edges between two nodes are permitted and might be specified through labels. Our notation was inspired by [17].

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    In addition to single words, even n-grams can be mapped to vectors. An n-gram is a set of n consecutive words. As outlined in Section 3.2 frequent co-occurrences of n consecutive words are identified and replaced by a single word e.g., machine learning is replaced by machine_learning.

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    A similar (but reversed problem) is the Skip-n-gram model i.e., to train a neural network to predict the representation of n context words from the representation of w. The Skip-n-gram approach can be summarised as “predicting the context given a word” while the CBOW, in a nutshell, is “predicting the word given a context”.

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    Py2neo Python library Available:

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    Though the same result could be achieved adding a property on the node, the use of multiple labels allows one to immediately access to the nodes with the desired label.

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    Performed through the stop words dictionary by the NLTK framework [10].

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    The edges selected using the Similarity label.

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    The idea is inspired by [7] though they compute the weight of triples through arithmetic functions.

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    The lower quartile is the 25th percentile while the upper quartile is the 75th percentile.

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Correspondence to Fabio Mercorio.

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Mezzanzanica, M., Mercorio, F., Cesarini, M. et al. GraphDBLP: a system for analysing networks of computer scientists through graph databases. Multimed Tools Appl 77, 18657–18688 (2018).

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  • Graph database
  • Word embedding
  • Knowledge extraction
  • Semantic analytics
  • Social network analysis