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Understanding Trolls with Efficient Analytics of Large Graphs in Neo4j

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dc.contributor.authorAllen, David
dc.contributor.authorHodler, Amy
dc.contributor.authorHunger, Michael
dc.contributor.authorKnobloch, Martin
dc.contributor.authorLyon, William
dc.contributor.authorNeedham, Mark
dc.contributor.authorVoigt, Hannes
dc.contributor.editorGrust, Torsten
dc.contributor.editorNaumann, Felix
dc.contributor.editorBöhm, Alexander
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorRahm, Erhard
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.contributor.editorMeyer, Holger
dc.date.accessioned2019-04-11T07:21:23Z
dc.date.available2019-04-11T07:21:23Z
dc.date.issued2019
dc.description.abstractAnalytics of large graph data set has become an important means of understanding and influencing the world. The use of graph database technology in the International Consortium of Investigative Journalists’ (ICIJ) investigation of the Panama Papers and Paradise Papers or in cancer research illustrates how analysing graph-structured data helps to uncover important but hidden relationships. A very current example in that regards shows how graph analytics can help shed light on the operations of social media troll-networks, e.g. on Twitter. In similar fashion, graph analytics can help enterprises to unearth hidden patterns and structures within connected data, to make more accurate predictions and faster decisions. All this requires efficient graph analytics well-integrated with management of graph data. The Neo4j Graph Platform provides such an environment. It provides transactional processing and analytical processing of graph data including data management and analytics tooling. A central element for graph analytics in the Graph Platform are the Neo4j graph algorithms. Neo4j graph algorithms provide efficiently implemented, parallel versions of common graph algorithms, integrated and optimized for the Neo4j transactional database. In this paper, we will describe the design and integration Neo4j Graph Algorithms, demonstrate its utility of our approach with a Twitter Troll analysis, and show case its performance with a few experiments on large graphs.en
dc.identifier.doi10.18420/btw2019-23
dc.identifier.isbn978-3-88579-683-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21708
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-289
dc.subjectGraph databases
dc.subjectgraph analytics
dc.subjectgraph algorithms
dc.subjectproperty graphs
dc.titleUnderstanding Trolls with Efficient Analytics of Large Graphs in Neo4jen
gi.citation.endPage396
gi.citation.startPage377
gi.conference.date4.-8. März 2019
gi.conference.locationRostock
gi.conference.sessiontitleIndustriebeiträge

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