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What and Who
Title:Algorithmic Building Blocks for Relationship Analysis over Large Graphs
Speaker:Stephan Seufert
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Language:English
Date, Time and Location
Date:Monday, 20 April 2015
Time:10:00
Duration:60 Minutes
Location:Saarbrücken
Building:E1 4
Room:024
Abstract
Over the last decade, large-scale graph datasets with millions of vertices and edges have emerged in many diverse problem domains. Notable examples include online social networks, the Web graph, or knowledge graphs connecting semantically typed entities. An important problem in this setting lies in the analysis of the relationships between the contained vertices, in order to gain insights into the structure and dynamics of the modeled interactions.

In this work, we develop efficient and scalable algorithms for three important problems in relationship analysis and make the following contributions:

• We present the Ferrari index structure to quickly probe a graph for the existence of an (indirect) relationship between two designated query vertices, based on an adaptive compression of the transitive closure of the graph.

• In order to quickly assess the relationship strength for a given pair of vertices as well as computing the corresponding paths, we present the PathSketch index structure for the fast approximation of shortest paths in large graphs. Our work extends a previously proposed prototype in several ways, including efficient index construction, compact index size, and faster query processing.

• We present the Espresso algorithm for characterizing the relationship between two sets of entities in a knowledge graph. This algorithm is based on the identification of important events from the interaction history of the entities of interest. These events are subsequently expanded into coherent subgraphs, corresponding to characteristic topics describing the relationship.

We provide extensive experimental evaluations for each of the methods, demonstrating the efficiency of the individual algorithms as well as their usefulness for facilitating effective analysis of relationships in large graphs.
Contact
Name(s):Petra Schaaf
Phone:5000
EMail:--email address not disclosed on the web
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Created by:Petra Schaaf/AG5/MPII/DE, 03/11/2015 09:39 AMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Petra Schaaf, 03/12/2015 10:50 AM
  • Petra Schaaf, 03/11/2015 01:50 PM -- Created document.